Deep Learning Course |TensorFlow Course |AI Training |Edureka

AI & Deep Learning with TensorFlow

Edureka's AI & Deep learning with TensorFlow Certification lets you gain expertise 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) and work with libraries like Keras & TFlearn.


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Why should you take AI & Deep Learning with TensorFlow 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
  • 3K + satisfied learners. Reviews

Instructor-led AI & Deep Learning with TensorFlow live online classes

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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 Engineer 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 Deep Learning using TensorFlow Certification Training.

In our TensorFlow Certification, you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello World” 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.

Note: This is intermediate to the advanced level course offered as part of Machine Learning program. It is assumed that you have taken the first course in Data Science Training and that you are familiar with Supervised Learning methods.

AI & Deep Learning with TensorFlow Certification is designed by industry experts for all those who want to learn Deep Leaning and master in TensorFlow applications. The AI & Deep Learning course offers:
  • In-depth knowledge of Deep Learning and Machine Learning Algorithms
  • Comprehensive knowledge of Neural Networks and Backpropagation
  • The capability to describe Recursive Neural Tensor Network Theory and implement Recursive Neural Network Model
  • Implementation of Collaborative Filtering with RBM, Keras, TFlearn
  • The exposure to real-life industry-based projects which will be executed using TensorFlow library
  • Rigorous involvement of an SME throughout the AI & Deep Learning Training to learn industry standards and best practices
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.
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.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to discover hidden structures within unlabelled and unstructured data.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life challenges.

This Deep Learning Training will help you learn Deep Leaning and master in TensorFlow applications. It will hone your skills by helping you to understand and analyze actual phenomena with Artificial Intelligence and provide the required hands-on experience for solving real-time industry-based projects.During this TensorFlow course, you will be trained by our expert instructors 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 variational 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

Edureka's Deep Learning with TensorFlow Certification is designed for all those who want to understand Deep Learning methods, Neural Networks, Deep Learning using 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 on 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 complimentary self-paced courses:
  • Statistics and Machine learning algorithms
  • Python Essentials
The system requirements for this Deep Learning with TensorFlow Certification are:
  • 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.
Edureka's TensorFlow Certification Training includes the following case studies:
  • Create an image classifier using CNN, and classify images in one of the predefined 100 classes
  • Create a script generator using LSTM, and generate scripts for any popular novel that might interest you
  • Choose a dataset of your own, explore the different challenges faced on the dataset domain and try to solve one of them with any neural network architecture covered in this course
Learning Objectives: In this module, you’ll get an introduction to Deep Learning and understand the limitations of Machine Learning. Also, you'll get familiar with the fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics. 

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
  • Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting, Overfitting and Optimization
Learning Objectives: 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. 

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
Learning Objectives: In this module, you’ll understand Backpropagation algorithm which is used for training Deep Networks. You'll know how Deep Learning uses neural network and Backpropagation to solve the problems which Machine Learning cannot. 

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
Learning Objectives: In this module, you’ll get started with the TensorFlow framework. You will understand it's working, various data types & functionalities. You will also learn to create an image classification model. 

Topics:
  • Why Deep Learning?
  • SONAR Dataset Classification
  • What is Deep Learning?
  • Feature Extraction
  • Working of Deep Network
  • Training using Backpropagation
  • Variants of Gradient Descent
  • Types of Deep Networks
Learning Objectives: 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. 

Topics:
  • Introduction to Convolutional Neural Networks
  • CNN Applications
  • Architecture of a Convolutional Neural Network
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks
Learning Objectives: 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, Recursive Neural Tensor Network Theory, and finally, you will learn to create an RNN model to solve a problem. 

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
Learning Objectives: 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. 

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

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
Learning Objectives: In this module, you’ll know how to use TFlearn API for implementing Neural Networks, understand various functions and features that TFlearn provide to make the task of neural network implementation easy. 

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
Learning Objectives: In this module, you will learn how to approach and implement a Machine project end to end. The instructor from the industry will share his experience and insights and help you kickstart your career in this domain. At last, we will be having a QA and doubt clearing session for the students.
. Call a Course Adviser for discussing Curriculum Details . 1844 230 6362
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.
We do provide placement assistance by routing relevant job opportunities to you as and when they come up. To get notified on relevant opportunities, it is important that you fill out your profile details.

It is important to attend classes and complete assignments. Course completion is an important criterion based on which we screen profiles of learners interested in a particular job. Also, before your profile is shared with prospective employers, you will have to go through an internal assessment by edureka. So it is important to be well versed with the course concepts to become eligible for placement opportunities.
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 have successfully submitted the TensorFlow Certification project, it will be reviewed by our expert panel. After a successful evaluation, you will be awarded Edureka's AI & Deep Learning with TensorFlow Expert Certificate.
  • Edureka's Certification has industry-wide 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.
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AI & Deep Learning with TensorFlow