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AI & Deep Learning with TensorFlow

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Why should you take AI & Deep Learning with TensorFlow course ?

  • Google stakes it's future on a piece of software: TensorFlow - MIT
  • TensorFlow is used heavily in Google's Speech Recognition System
  • 13K + satisfied learners. Reviews
  • 60 days of   Cloud Lab   access
About the Course

Edureka's Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will 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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Instructor-led AI & Deep Learning with TensorFlow live online classes

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Introduction to Deep Learning

    Learning Objectives:
    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.

    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 and Overfitting, Optimization

    • Hands-On

  • Implementing a Linear Regression model for predicting house prices from Boston dataset
  • Implementing a Logistic Regression model for classifying Customers based on a Automobile purchase dataset

Understanding Neural Networks with TensorFlow

    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

    • Hands-On

  • Building a single perceptron for classification on SONAR dataset

Deep dive into Neural Networks with TensorFlow

    Learning Objectives:

    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 that 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

    • Hands-On

  • Building a multi-layered perceptron for classification of Hand-written digits

Master Deep Networks

    Learning Objectives:

    In this module, you’ll get started with the TensorFlow framework. You will understand how it works, its various data types & functionalities. In addition, you will create an image classification model.

    Topics:

  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation on SONAR dataset
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent
  • Types of Deep Networks

    • Hands-On

  • Building a multi-layered perceptron for classification on SONAR dataset

Convolutional Neural Networks (CNN)

    Learning Objectives:

    In this module, you’ll understand convolutional neural networks and its applications. You will learn the working of CNN, and create a CNN model to solve a problem.

    Topics:

  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN

    • Hands-On

  • Building a convolutional neural network for image classification. The model should predict the difference between 10 categories of images.

Recurrent Neural Networks (RNN)

    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, what is Recursive Neural Tensor Network Theory, and finally you will learn to create a RNN model.

    Topics:

  • Introduction 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

    • Hands-On

  • Building a recurrent neural network for SPAM prediction.

Restricted Boltzmann Machine (RBM) and Autoencoders

    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

    • Hands-On

  • Building a Autoencoder model for classification of handwritten images extracted from the MNIST Dataset

Keras API

    Learning Objectives:

    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 provides 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

    • Hands-On

  • Build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio

TFLearn API

    Learning Objectives:

    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 provides 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

    • Hands-On

  • Build a recurrent neural network using TFLearn to do image classification on hand-written digits

In-Class Project

    Learning Objectives:

    In this module, you should learn how to approach and implement a  project end to end. The instructor  will share his industry experience and related insights that will help you kickstart your career in this domain. In addition, we will be having a QA and doubt clearing session for you.

    Topics:

  • How to approach a project? 
  • Hands-On project implementation
  • What Industry expects?
  • Industry insights for the Machine Learning domain
  • QA and Doubt Clearing Session
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About Deep Learning in TensorFlow with Python Certification Training

In this Deep Learning in TensorFlow with Python Training we will learn about what is AI, explore  neural networks, understand deep learning frameworks, implement various machine learning algorithms using Deep Networks. We will also explore how different layers in neural networks does data abstraction and feature extraction using Deep Learning.

Edureka’s Deep Learning in TensorFlow training is designed to make you a Data Scientist by providing you rich hands-on training on Deep Learning in TensorFlow with Python. This course is a stepping stone in your Data Science journey using which you will get the opportunity to work on various Deep Learning projects.

What are the objectives of our Deep Learning In TensorFlow with Python Course?

Deep Learning in TensorFlow with Python Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers:
  • In-depth knowledge of Deep Neural Networks
  • Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders
  • Implementation of Collaborative Filtering with RBM
  • 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

Why Should you go for Deep Learning in TensorFlow with Python Certification Training?

Deep Learning is one of the most accelerating and promising fields, among all the technologies available in the IT market today. To become an expert in this technology, you need a structured training with the latest skills as per current industry requirements and best practices.

Besides strong theoretical understanding, you will be working on various real-life data projects using different neural network architectures as a part of solution strategy.

Additionally, you will receive guidance from a Deep Learning expert who is currently working in the industry on real-life projects.

What are the skills that you will be learning with our Deep Learning in TensorFlow Certification Training?

Deep Learning in TensorFlow with Python Training will help you to become a Deep Learning Engineer. It will hone your skills by offering you comprehensive knowledge on Deep Learning in TensorFlow. It will also acquaint you with the required hands-on experience for solving real-time industry-based Deep Learning projects. During this course you will be trained by our expert instructors on:

  • Deep Learning and TensorFlow Concepts
  • Working with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)
  • Proficiency in Long short-term memory (LSTM)
  • Implementing Keras, TFlearn, Autoencoders
  • Implementing Restricted Boltz-mann Machine (RBM)
  • Knowledge of Neural Networks & Natural Language Processing (NLP)
  • Using Python with TensorFlow Libraries
  • Perform Text Analytics
  • Perform Text Processing

Who should go for this Deep Learning in TensorFlow with Python Course?

The TensorFlow with Python Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer. It is best suited for individuals who are:

  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Deep Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • Analysts wanting to understand Data Science methodologies

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

What are the pre-requisites for this Deep Learning in TensorFlow with Python Course?

Required Pre-requisites

  • Basic programming knowledge in Python
  • Concepts about Machine Learning

    • Edureka offers you complimentary self-paced courses:

  • Statistics and Machine learning algorithms
  • Python Essentials

Edureka’s Deep Learning Engineer Certificate Holders work at 1000s of companies like

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80% Course completion rate
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How will I execute the practicals?

You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

What is CloudLab?

CloudLab is a cloud-based Jupyter Notebook which is pre-installed with TensorFlow and Python packages on cloud-lab environment. It is offered by Edureka as a part of Deep Learning with TensorFlow course where you can execute all the in-class demos and work on real-life projects in a fluent manner.

You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.

What are the projects included in this Deep Learning in TensorFlow with Python Certification Training?

    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

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.

Cloud Lab New

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment

What if I miss a class?

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.

Will I get placement assistance?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.

Can I attend a demo session before enrollment?

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.

Who are the instructors?

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.

What if I have more queries?

Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co

How do I avail EMI option as a method of payment?

You no longer need a credit history or a credit card to purchase this course. Using ZestMoney, we allow you to complete your payment with a EMI plan that best suits you. It's a simple 3 step procedure:
  • Fill your profile: Complete your profile with Aadhaar, PAN and employment details.
  • Verify your account: Get your account verified using netbanking, ekyc or uploading documents
  • Activate your loan: Setup automatic repayment using NACH to activate your loan
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