Deep Learning Course |TensorFlow Course |AI Training |Edureka

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 Keras, TFlearn, 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
  • 2K + satisfied learners. Reviews

Instructor-led live online classes

25

Nov
Sat - Sun ( 5 Weeks )
10:00 AM - 01:00 PM ( EST )
19995

17

Dec
Sun - Thu ( 15 Days )
08:30 PM - 10:30 PM ( EST )
19995

29

Dec
Fri - Sat ( 5 Weeks )
09:30 PM - 12:30 AM ( EST )
19995
Starts on 24th Nov 2017, 09:30 PM EST

Data Science Masters Program covers a broad array of topics which includes: Data extraction, Exploration techniques, Machine Learning algorithms, Python, Apache Spark, Deep learning and many more.

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32

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Train your employees with exclusive batches and offers and track your employee's progress with our weekly progress report.

Instructor-led Sessions

30hrs of Online Live Instructor-led Classes. Weekend class: 10 sessions of 3 hours each. Weekday class: 15 sessions of 2 hours each  

Real-life Case Studies

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

Assignments

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.

Certification

  • 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.

Forum

We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning using TensorFlow Certification Training.

In our Tensorflow Training, you will be able 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 

Note: This is an intermediate to advanced level course offered as part of Machine Learning  program. It assumes you have taken a first course in Data science training, and that you are at least familiar with supervised learning methods.
After the completion of this Training, 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
  • Understand Keras Implementation
  • Understand TFlearn implementation
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 unlabelled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressional, 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.

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​s​​:​
    • Statistics and ​Machine learning algorithms
    • Python Essentials
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.
  • To create an image classifier using CNN, to classify images in one of the predefined 100 classes
  • To create a script generator using LSTM, for generating scripts for any popular novel that you might interest you
  • Capstone project, here you can choose a dataset of your own, explore the different challenges faced on the dataset domain and try to solve any one of them with any neural network architecture covered in the course
Goal:  In  this  module,  you’ll  get  an  introduction to  Deep  Learning  and  understand  how  Deep Learning  solves  problems  which  Machine Learning  cannot.  Understand  fundamentals  of Machine  Learning  and  relevant  topics  of  Linear Algebra  and  Statistics.

Objectives:
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

Topics:
•  Deep  Learning:  A  revolution  in  Artificial  Intelligence
•  Limitations  of  Machine  Learning
•  What  is  Deep  Learning?
•  Advantage  of  Deep  Learning  over  Machine  learning
•  3  Reasons  to  go  for  Deep  Learning
•  Real-Life  use  cases  of  Deep  Learning
•  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  
o  Regression
o  Classification
o  Clustering
o  Reinforcement  Learning  
o  Underfitting  and  Overfitting
o  Optimization
Goal: In this module, you’ll get an introduction to Neural Networks and understand it’s working i.e. how it is trained, what are the various parameters considered for its training and the activation functions that are applied.

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

• Illustrate How Deep Learning Works?
• How Neural Networks Work?
• Understand Various Components of a Neural Network
• Define TensorFlow
• Illustrate How TensorFlow works
• Discuss the Functionalities of TensorFlow
• Implement a Single Layer Perceptron using TensorFlow

Topics:
• How Deep Learning Works?
• Activation Functions
• Illustrate Perceptron
• Training a Perceptron
• Important Parameters of Perceptron
• What is Tensorflow?
• Tensorflow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
• Creating a Model
• Step by Step - Use-Case Implementation
Goal: In this module, you’ll understand backpropagation algorithm which is used for training Deep Networks. You will know how Deep Learning uses neural network and backpropagation to solve the problems which Machine Learning cannot. 

Objectives:
At the end of this Module, you should be able to:
• Understand limitations of A Single Perceptron
• Illustrate Working of Multi-Layered Perceptron (MLP)
• Understand MLP Training Phases
• Illustrate How TensorFlow works
• Discuss the Functionalities of TensorFlow
• Implement a Multi-Layered Perceptron using TensorFlow

Topics:
• Understand limitations of A Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• Backpropagation – Learning Algorithm
• Understand Backpropagation – Using Neural Network Example
• MLP Digit-Classifier using TensorFlow
• TensorBoard
• Summary
Goal: In this module, you’ll get started with the TensorFlow framework. You will understand how it works, its various data types & functionalities. You will learn to create an image classification model.

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

• Why Deep Learning? 
• What is Deep Learning? 
• Feature Extraction 
• Working of a Deep Network 
• Training using Backpropagation 
• Variants of Gradient Descent 
• Types of Deep Networks 

Topics:
• Why Deep Learning? 
• SONAR Dataset Classification 
• What is Deep Learning? 
• Feature Extraction 
• Working of a Deep Network 
• Training using Backpropagation 
• Variants of Gradient Descent 
• Types of Deep Networks
Goal: In this module, you’ll understand convolutional neural networks and its applications. You will understand the working of CNN, and create a CNN model to solve a problem.

Objectives:
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

Topics:
• 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
Goal: In this module, you’ll understand Recurrent Neural Networks and its applications. You will understand the working of RNN, how LSTM are used in RNN, what is Recursive Neural Tensor Network Theory, and finally you will learn to create a RNN model to solve a problem.

Objectives:
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

Topics:
• 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
Goal: In this module, you’ll understand RBM & Autoencoders along with their applications. You will understand the working of RBM & Autoencoders, illustrate Collaborative Filtering using RBM and understand what are Deep Belief Networks.

Objectives:
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

Topics:
• Restricted Boltzmann Machine
• Applications of RBM
• Collaborative Filtering with RBM
• Introduction to Autoencoders 
• Autoencoders applications
• Understanding Autoencoders
Goal: In this module, you’ll understand how to use Keras API for implementing Neural Networks, the goal is to understand various functions and features that Keras provide to make the task of neural network implementation easy. 

Objectives:
At the end of this Module, you should be able to:
• Define Keras 
• Understand Keras Model Building Blocks
• Illustrate Different Compositional Layers for a Keras Model
• Implement a Use-Case Step by Step
• Understand few features available with Keras

Topics:
• Define Keras
• How to compose Models in Keras
• Sequential Composition 
• Functional Composition
• Predefined Neural Network Layers
• What is Batch Normalization
• Saving and Loading a model with Keras
• Customizing the Training Process
• Using TensorBoard with Keras
• Use-Case Implementation with Keras
Goal: In this module, you’ll understand how to use TFlearn API for implementing Neural Networks, the goal is to understand various functions and features that TFlearn provide to make the task of neural network implementation easy. 

Objectives:
At the end of this Module, you should be able to:
• Define TFlearn 
• Understand TFlearn Model Building Blocks
• Illustrate Different Compositional Layers for a TFlearn Model
• Implement a Use-Case Step by Step
• Understand few features available with TFlearn

Topics:
• Define TFlearn
• Composing Models in TFlearn
• Sequential Composition 
• Functional Composition
• Predefined Neural Network Layers
• What is Batch Normalization
• Saving and Loading a model with TFlearn
• Customizing the Training Process
• Using TensorBoard with TFlearn
• Use-Case Implementation with TFlearn
Goal : In this module, you should learn how to approach and implement a Machine project end to end, the instructor from the industry will share his experience and insights from the industry to help you kickstart your career in this domain. At last we will be having a QA and doubt clearing session for the students. 

Objectives - At the end of this module, you should be able to:
How to approach a project
Hands-On project implementation
What Industry expects
Industry insights for the Machine Learning domain
QA and Doubt Clearing Session

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 enrollment 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 a 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 to the participants.

You can Call us at +91 90660 20867 /1844 230 6362 ( US Tollfree ) OR Email us at sales@edureka.co . We shall be glad to assist you.


  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s AI & Deep Learning with 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.

AI & Deep Learning with TensorFlow