# Udemy - Master Deep Learning with TensorFlow in Python

#### Category: Technical

#### Tag: Office

Posted on 2019-05-16, by phaelx.

Description

Date: March 2019

Author: 365 Careers

Size: 1.4 GB

Format: MP4

Author: 365 Careers

Size: 1.4 GB

Format: MP4

Download >> https://dropapk.com/dhki8wpvawh4

What you'll learn

*Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework

*Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow

*Set Yourself Apart with Hands-on Deep and Machine Learning Experience

*Grasp the Mathematics Behind Deep Learning Algorithms

*Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules

*Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization

*Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding

Course content

Welcome! Course introduction

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the working environment

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - your first machine learning algorithm

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow - An introduction

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Going deeper: Introduction to deep neural networks

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Overfitting

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Gradient descent and learning rates

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The MNIST example

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Business case

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

Appendix: Linear Algebra Fundamentals

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

What is a Matrix?

Scalars and Vectors

Linear Algebra and Geometry

Scalars, Vectors and Matrices in Python

Tensors

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product of Vectors

Dot Product of Matrices

Why is Linear Algebra Useful?

Conclusion

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

What is a Matrix?

Scalars and Vectors

Linear Algebra and Geometry

Scalars, Vectors and Matrices in Python

Tensors

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product of Vectors

Dot Product of Matrices

Why is Linear Algebra Useful?

See how much you have learned

What's further out there in the machine and deep learning world

An overview of CNNs

How DeepMind uses deep learning

An overview of RNNs

An overview of non-NN approaches

Bonus lecture

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

What is a Matrix?

Scalars and Vectors

Linear Algebra and Geometry

Scalars, Vectors and Matrices in Python

Tensors

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product of Vectors

Dot Product of Matrices

Why is Linear Algebra Useful?

See how much you have learned

What's further out there in the machine and deep learning world

An overview of CNNs

How DeepMind uses deep learning

An overview of RNNs

An overview of non-NN approaches

Bonus lecture: Next steps

*Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework

*Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow

*Set Yourself Apart with Hands-on Deep and Machine Learning Experience

*Grasp the Mathematics Behind Deep Learning Algorithms

*Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules

*Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization

*Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding

Course content

Welcome! Course introduction

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the working environment

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - your first machine learning algorithm

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow - An introduction

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Going deeper: Introduction to deep neural networks

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Overfitting

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Gradient descent and learning rates

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The MNIST example

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Business case

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

Appendix: Linear Algebra Fundamentals

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

What is a Matrix?

Scalars and Vectors

Linear Algebra and Geometry

Scalars, Vectors and Matrices in Python

Tensors

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product of Vectors

Dot Product of Matrices

Why is Linear Algebra Useful?

Conclusion

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

What is a Matrix?

Scalars and Vectors

Linear Algebra and Geometry

Scalars, Vectors and Matrices in Python

Tensors

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product of Vectors

Dot Product of Matrices

Why is Linear Algebra Useful?

See how much you have learned

What's further out there in the machine and deep learning world

An overview of CNNs

How DeepMind uses deep learning

An overview of RNNs

An overview of non-NN approaches

Bonus lecture

Meet your instructors and why you should study machine learning?

What does the course cover?

What does the course cover? - Quiz

Introduction to neural networks

Introduction to neural networks - Quiz

Training the model

Training the model - Quiz

Types of machine learning

Types of machine learning - Quiz

The linear model

The linear model - Quiz

Need Help with Linear Algebra?

The linear model. Multiple inputs

The linear model. Multiple inputs - Quiz

The linear model. Multiple inputs and multiple outputs

The linear model. Multiple inputs and multiple outputs - Quiz

Graphical representation

Graphical representation - Quiz

The objective function

The objective function - Quiz

L2-norm loss

L2-norm loss - Quiz

Cross-entropy loss

Cross-entropy loss - Quiz

One parameter gradient descent

One parameter gradient descent - Quiz

N-parameter gradient descent

N-parameter gradient descent - Quiz

Setting up the environment - An introduction - Do not skip, please!

Why Python and why Jupyter?

Why Python and why Jupyter? - Quiz

Installing Anaconda

The Jupyter dashboard - part 1

The Jupyter dashboard - part 2

Jupyter Shortcuts

The Jupyter dashboard - Quiz

Installing the TensorFlow package

Installing packages - exercise

Installing packages - solution

Minimal example - part 1

Minimal example - part 2

Minimal example - part 3

Minimal example - part 4

Minimal example - Exercises

TensorFlow outline

TensorFlow intro

Types of file formats in TensorFlow

Inputs, outputs, targets, weights, biases - model layout

Loss function and gradient descent - introducing optimizers

Model output

Minimal example - Exercises

Layers

What is a deep net?

Understanding deep nets in depth

Why do we need non-linearities?

Activation functions

Softmax activation

Backpropagation

Backpropagation - visual representation

Backpropagation. A peek into the Mathematics of Optimization

Underfitting and overfitting

Underfitting and overfitting - classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization - Introduction

Types of simple initializations

Xavier initialization

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The dataset

How to tackle the MNIST

Importing the relevant packages

Outlining the model

Declaring the loss and the optimization algorithm

Accuracy of prediction

Batching and early stopping

Learning

Discuss the results and test

MNIST - exercises

MNIST - solutions

Exploring the dataset and identifying predictors

Outlining the business case solution

Balancing the dataset

Preprocessing the data

Preprocessing exercise

Create a class for batching

Outlining the model

Optimizing the algorithm

Interpreting the result

Testing the model

A comment on the homework

Final exercise

What is a Matrix?

Scalars and Vectors

Linear Algebra and Geometry

Scalars, Vectors and Matrices in Python

Tensors

Addition and Subtraction of Matrices

Errors when Adding Matrices

Transpose of a Matrix

Dot Product of Vectors

Dot Product of Matrices

Why is Linear Algebra Useful?

See how much you have learned

What's further out there in the machine and deep learning world

An overview of CNNs

How DeepMind uses deep learning

An overview of RNNs

An overview of non-NN approaches

Bonus lecture: Next steps

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