Deep Learning with Keras and Tensorflow in Python and R

Category: Tutorial

Posted on 2020-04-01, by 0nelovee.


[b]Deep Learning with Keras and Tensorflow in Python and R[b]
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 72 lectures (10 hour, 51 mins) | Size: 4 GB

Understand Deep Learning and build Neural Networks using

TensorFlow 2.0 and Keras in Python and R
What you'll learn

Get a solid understanding of Artificial Neural Networks (ANN) and

Deep Learning
Learn usage of Keras and Tensorflow libraries
Understand the business scenarios where Artificial Neural Networks

(ANN) is applicable
Building a Artificial Neural Networks (ANN) in Python and R
Use Artificial Neural Networks (ANN) to make predictions


Students will need to install Python and Anaconda software but we

have a separate lecture to help you install the sameStudents will

need to install R, Python and Anaconda software but we have a

separate lecture to help you install the same


You're looking for a complete Course on Deep Learning using Keras

and Tensorflow that teaches you everything you need to create a

Neural Network model in Python and R, right?

You've found the right Neural Networks course!

After completing this course you will be able to:

Identify the business problem which can be solved using Neural

network Models.

Have a clear understanding of Advanced Neural network concepts

such as Gradient Descent, forward and Backward Propagation etc.

Create Neural network models in Python and R using Keras and

Tensorflow libraries and analyze their results.

Confidently practice, discuss and understand Deep Learning


How this course will help you?

A Verifiable Certificate of Completion is presented to all

students who undertake this Neural networks course.

If you are a business Analyst or an executive, or a student who

wants to learn and apply Deep learning in Real world problems of

business, this course will give you a solid base for that by

teaching you some of the most advanced concepts of Neural networks

and their implementation in Python without getting too


Why should you choose this course?

This course covers all the steps that one should take to create a

predictive model using Neural Networks.

Most courses only focus on teaching how to run the analysis but we

believe that having a strong theoretical understanding of the

concepts enables us to create a good model . And after running the

analysis, one should be able to judge how good the model is and

interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in

Global Analytics Consulting firm, we have helped businesses solve

their business problem using Deep learning techniques and we have

used our experience to include the practical aspects of data

analysis in this course

We are also the creators of some of the most popular online

courses - with over 250,000 enrollments and thousands of 5-star

reviews like these ones:

This is very good, i love the fact the all explanation given can

be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and

this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If

you have any questions about the course content, practice sheet or

anything related to any topic, you can always post a question in

the course or send us a direct message.

Download Practice files, take Practice test, and complete


With each lecture, there are class notes attached for you to

follow along. You can also take practice test to check your

understanding of concepts. There is a final practical assignment

for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network

based model i.e. a Deep Learning model, to solve business


Below are the course contents of this course on ANN:

Part 1 - Python and R basics

This part gets you started with Python.

This part will help you set up the python and Jupyter environment

on your system and it'll teach you how to perform some basic

operations in Python. We will understand the importance of

different libraries such as Numpy, Pandas & Seaborn.

Part 2 - Theoretical Concepts

This part will give you a solid understanding of concepts involved

in Neural Networks.

In this section you will learn about the single cells or

Perceptrons and how Perceptrons are stacked to create a network

architecture. Once architecture is set, we understand the Gradient

descent algorithm to find the minima of a function and learn how

this is used to optimize our network model.

Part 3 - Creating Regression and Classification ANN model in

Python and R

In this part you will learn how to create ANN models in Python.

We will start this section by creating an ANN model using

Sequential API to solve a classification problem. We learn how to

define network architecture, configure the model and train the

model. Then we evaluate the performance of our trained model and

use it to predict on new data. We also solve a regression problem

in which we try to predict house prices in a location. We will

also cover how to create complex ANN architectures using

functional API. Lastly we learn how to save and restore models.

We also understand the importance of libraries such as Keras and

TensorFlow in this part.

Part 4 - Data Preprocessing

In this part you will learn what actions you need to take to

prepare Data for the analysis, these steps are very important for

creating a meaningful.

In this section, we will start with the basic theory of decision

tree then we cover data pre-processing topics like missing value

imputation, variable transformation and Test-Train split.

By the end of this course, your confidence in creating a Neural

Network model in Python will soar. You'll have a thorough

understanding of how to use ANN to create predictive models and

solve business problems.

Go ahead and click the enroll button, and I'll see you in lesson



Start-Tech Academy


Below are some popular FAQs of students who want to start their

Deep learning journey-

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a

career in Deep Learning.

Though it hasn�t always been, Python is the programming language

of choice for data science. Here�s a brief history:

In 2016, it overtook R on Kaggle, the premier platform for data

science competitions.

In 2017, it overtook R on KDNuggets�s annual poll of data

scientists� most used tools.

In 2018, 66% of data scientists reported using Python daily,

making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with

increasing development in the Python ecosystem. And while your

journey to learn Python programming may be just beginning, it�s

nice to know that employment opportunities are abundant (and

growing) as well.

What is the difference between Data Mining, Machine Learning, and

Deep Learning?

Put simply, machine learning and data mining use the same

algorithms and techniques as data mining, except the kinds of

predictions vary. While data mining discovers previously unknown

patterns and knowledge, machine learning reproduces known patterns

and knowledge�and further automatically applies that information

to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power

and special types of neural networks and applies them to large

amounts of data to learn, understand, and identify complicated

patterns. Automatic language translation and medical diagnoses are

examples of deep learning.

Who this course is for:

People pursuing a career in data science
Anyone curious to master ANN from Beginner level in short span of time

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