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
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


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