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AI & Deep Learning with TensorFlow

Edureka's AI & Deep learning with Tensorflow course will make you an expert in training and optimizing basic and convolutional neural networks using real time projects and assignments. You will also master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM).

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Why this course ?

  • TensorFlow could be a game-changer in the future of AI - Google
  • Google open-sources TensorFlow for deep learning with big data
  • Google gives everyone machine learning superpowers with TensorFlow
  • 806 + satisfied learners. Reviews

Instructor-led live online classes


Fri - Sat ( 4 Weeks )
09:30 PM - 12:30 AM ( EDT )
10% Off


Sat - Sun ( 4 Weeks )
11:00 AM - 02:00 PM ( EDT )
10% Off
Starts on 23rd Jul' 2017, 09:30 PM EDT

e! Masters Program offers an in-depth knowledge of Hadoop ecosystem, Deep Learning with Tensor Flow, real-time processing using Spark & database concepts.

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Instructor-led Sessions

21hrs of Online Live Instructor-led Classes. Weekend class: 7 sessions of 3 hours each 

Real-life Case Studies

Towards the end of the Course, you will work on a real life case study.


Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during the class.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have 24x7 online support team available to help you with any technical queries you may have during the course.


  • End of the course, you will be working on a project. Edureka certifies you as a Deep Learning with Tensorflow Expert based on the project.


We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.

Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. 

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

After the completion of this Deep Learning with TensorFlow course,  you should be able to:
  • Define Deep Learning
  • Express the motivation behind Deep Learning
  • Apply Analytical mathematics on the data
  • Choose between different Deep networks
  • Explain Neural networks
  • Train Neural networks
  • Discuss Backpropagation
  • Describe Autoencoders and varitional Autoencoders
  • Run a “Hello World” program in TensorFlow
  • Implement different Regression models
  • Describe Convolutional Neural Networks
  • Discuss the application of Convolutional Neural Networks
  • Discuss Recurrent Neural Networks
  • Describe Recursive Neural Tensor Network Theory
  • Implement Recursive Neural Network Model
  • Explain Unsupervised Learning
  • Discuss the applications of Unsupervised Learning
  • Explain Restricted Boltzmann Machine
  • Implement Collaborative Filtering with RBM
  • Define Autoencoders and discuss their Applications
  • Discuss Deep Belief Network

TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. 

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

Edureka Deep learning with Tensorflow course is designed for all those who want to learn Deep Leaning which would include understanding of Deep Learning methods, Neural Networks, Deep Learning uses Tensorflow, Restricted Boltzmann Machines (RBM) and Autoencoders.

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts 

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

Required Pre-requisites
  • Basic programming knowledge in Python
  • Concept of Arrays
  • Concepts about Machine Learning

Edureka offers you a complimentary self-paced course - A Module on Stats and Machine learning algorithms: Supervised and Unsupervised learning algorithms, once you have enrolled in Deep Learning with TensorFlow course
The system requirements for Deep Learning with Tensorflow course is Multicore Processor (i3-i7 series), 8GB of RAM is recommended and 15 GB of free disk space. The operating system can be Windows, Linux or Mac OS X.
For executing the practical, you will set-up Tensorflow library on your machine, which can be installed on any operating system that is (Windows, Linux or Mac OS X). The detailed step by step installation guides will be present in your LMS which will help you to install and set-up the required environment. In case you come across any doubt, the 24*7 support team will promptly assist you.


At the end of this Module, you should be able to:

Discuss the revolution of Artificial Intelligence

Discuss the limitations of Machine Learning

List the advantages of Deep Learning over Machine Learning

Discuss Real-life use cases of Deep Learning

Understand the Scenarios where Deep Learning is applicable

Discuss relevant topics of Linear Algebra and Statistics

Discuss Machine learning algorithms

Define Reinforcement Learning 

Discuss model parameters and optimization techniques


Deep Learning: A revolution in Artificial Intelligence

Limitations of Machine Learning

Discuss the idea behind Deep Learning

Advantage of Deep Learning over Machine learning

3 Reasons to go Deep

Real-Life use cases of Deep Learning

Scenarios where Deep Learning is applicable

The Math behind Machine Learning: Linear Algebra

o Scalars

o Vectors

o Matrices

o Tensors

o Hyperplanes

The Math Behind Machine Learning: Statistics

o Probability

o Conditional Probabilities

o Posterior Probability

o Distributions

o Samples vs Population

o Resampling Methods

o Selection Bias

o Likelihood

Review of Machine Learning Algorithms

o Regression

o Classification

o Clustering

Reinforcement Learning 

Underfitting and Overfitting


Convex Optimization


At the end of this Module, you should be able to:

Define Neural Networks

Discuss the Training Techniques of Neural Networks

List Different Activation and Loss Functions

Discuss the Different parameters of Neural Networks 


Defining Neural Networks

The Biological Neuron

The Perceptron

Multi-Layer Feed-Forward Networks

Training Neural Networks

Backpropagation Learning

Gradient Descent

Stochastic Gradient Descent

Quasi-Newton Optimization Methods

Generative vs Discriminative Models

Activation Functions

o Linear

o Sigmoid

o Tanh

o Hard Tanh

o Softmax

o Rectified Linear

Loss Functions

Loss Function Notation

Loss Functions for Regression

Loss Functions for Classification

Loss Functions for Reconstruction


Learning Rate





At the end of this Module, you should be able to:

Define Deep Learning

Discuss the Architectural Principals of Deep Networks

List Different parameters of Deep Networks

Discuss the Building Blocks of Deep Networks

Discuss how reinforcement learning is used in Deep Networks


Defining Deep Learning

Defining Deep Networks

Common Architectural Principals of Deep Networks

Reinforcement Learning application in Deep Networks



Activation Functions - Sigmoid, Tanh, ReLU

Loss Functions

Optimization Algorithms




At the end of this Module, you should be able to:

Define TensorFlow

Illustrate how TensorFlow works

Discuss the Functionalities of TensorFlow

Illustrate different ways to install TensorFlow

Write and Run programs on TensorFlow


What is TensorFlow?

Use of TensorFlow in Deep Learning

Working of TensorFlow

How to install Tensorflow

HelloWorld with TensorFlow

Running a Machine learning algorithms on TensorFlow


At the end of this Module, you should be able to:

Define CNNs

Discuss the Applications of CNN

Explain the Architecture of a CNN

List Convolution and Pooling Layers in CNN

Illustrate CNN

Discuss Fine-tuning and Transfer Learning of CNNs


Introduction to CNNs

CNNs Application

Architecture of a CNN

Convolution and Pooling layers in a CNN

Understanding and Visualizing a CNN

Transfer Learning and Fine-tuning Convolutional Neural Networks


At the end of this Module, you should be able to:

Define RNN

Discuss the Applications of RNN

Illustrate how RNN is trained

Discuss Long Short-Term memory(LSTM)

Explain Recursive Neural Tensor Network Theory

Illustrate the working of Neural Network Model


Intro to RNN Model

Application use cases of RNN 

Modelling sequences

Training RNNs with Backpropagation 

Long Short-Term memory (LSTM)

Recursive Neural Tensor Network Theory

Recurrent Neural Network Model


At the end of this Module, you should be able to:

Define RBM

Discuss the Applications of RBM

Illustrate Collaborative Filtering using RBM 

Define Autoencoders

Explain Deep Belief Networks


Restricted Boltzmann Machine

Applications of RBM

Collaborative Filtering with RBM

Introduction to Autoencoders 

Autoencoders applications

Understanding Autoencoders

Variational Autoencoders

Deep Belief Network

"You will never miss a lecture at Edureka! You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch."
edureka is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience.

You can give us a CALL at +91 88808 62004/1800 275 9730 (US Tollfree Number) OR email at sales@edureka.co

  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Tensorflow Expert certificate.
  • edureka certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc. Please be assured.