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AI & Deep Learning with TensorFlow (Virtual Instructor-led Training)

> TensorFlow could be a game-changer in the future of AI - Google

> Google stakes it's future on a piece of software: TensorFlow - MIT

> TensorFlow is used heavily in Google's Speech Recognition System

USD 449 USD 499

Course Overview

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. The course has been specially curated by industry experts with real-time case studies.

Key Highlights

  • 30hrs of Online Live Instructor-led Classes
  • Weekend class: 10 sessions of 3 hours each 
  • Weekday class: 15 sessions of 2 hours
  • Real-life Case Studies
  • Assignments
  • lifetime access to the Learning Management System (LMS)
  • 24 x 7 Expert Support
  • Certification
  • Community forum for all our Learners
  • Industry based Projects
  • No exam Included

What You'll Learn

  • 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

SCHEDULE

    • Delivery Format: Virtual Classroom Live
    • Location: Online
    • Access Period: 5 Weeks
    • Course Date: OCT 30 th
    • Course Time: 09:30 PM to 12:30 AM (EDT)
    • Session: Weekdays
    • Total Class: FRI & SAT (10 Sessions)
    QTY
    USD 449 USD 499
    • Delivery Format: Virtual Classroom Live
    • Location: Online
    • Access Period: 5 Weeks
    • Course Date: NOV 21 st
    • Course Time: 11:00 AM to 02:00 PM (EDT
    • Session: Weekend
    • Total Class: SAT & SUN (10 Sessions)
    QTY
    USD 449 USD 499

Career Benefits

  • Used in large to small IT companies
  • Opens up better opportunities
  • Higher paycheck

Who Can Attend

  • 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

Exam Formats

No Exam Included.

Course Delivery

This course is available in the following formats:

  • Virtual Classroom Live Duration: 30 Hrs

Related Courses

Course Syllabus


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

FAQ's


What if I miss a class?

You will never miss a lecture at Upskill Yourself! 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.

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.

Why learn AI & Deep Learning with TensorFlow?

Deep Learning is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. The software industry now-a-days is moving towards machine intelligence. In fact, concepts within AI like Deep Learning with TensorFlow has become necessary in every sector generating lot of job opportunities worldwide.

What are the skills needed to master Deep Learning with Tensorflow?

In order to master Deep Learning with Tensorflow you need following skills:

Understanding of Tensorflow concepts, its main functions, operations and the pipeline of execution

Building and training Deep Learning Models using TensorFlow

Understanding the working of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) and their applications

Proficiency in working with TFlearn, Autoencoders, Keras

Improving accuracy of deep learning models

Implementation of Restricted Boltz-mann Machine (RBM)

Performing Text Processing and Analysis

Mike Williams, Direct Consultant