AI & Deep Learning with TensorFlow

Extensive package of Machine Learning, Deep Learning, and Artificial Intelligence

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Key Highlights

  • 30 hours of Instructor-led online live sessions
  • 10 sessions of 3 hours each (Weekend)
  • Complimentary self-paced courses of Statistics and Machine learning algorithms and Python Essentials
  • Case Studies on Real-life Scenarios
  • Lifetime Access to Learning Management System
  • Practice Assignments
  • 24X7 Expert Support
  • Online Forum for Discussions
  • Cloud lab for real-life experience

Course Price Range

$389.00 - $389.00 $800.00 - $800.00

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1866-216-7898

(Toll Free)

Available Courses Delivery

This course is available in the following formats:

Virtual Live

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Nov 23rd
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Delivery: Online
Access: Lifetime
Sat - Sun (5 Weeks)
Timings - 10:00 AM to 01:00 PM (EST)

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$800  $389
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Course Overview

AI & Deep learning with TensorFlow certification course is designed to acquaint learners with Artificial Intelligence and Machine Learning technologies. It helps learners gain practical knowledge to develop Deep Learning models using TensorFlow. It aims to develop proficiency of learners in concepts, such as, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM), SoftMax function. The course also introduces learners to Keras API and TFLearn API.

Course Objectives

  • Acquaint learners with fundamentals of Deep Learning techniques
  • Teach learners about Deep Neural Networks
  • Train learners in various Neural Network architecture, such as, Convolutional Neural Network, Autoencoders, Recurrent Neural Network
  • Equip learners with Collaborative Filtering and its implementation
  • Educate learners about Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
  • Acquaint learners with Keras API and TFLearn API

Career Benefits

  • Become a part of one of the leading fields in the IT sector
  • Numerous possibilities to work on various AI & Deep Learning projects
  • Gain expertise in CNN and RNN
  • Proficiency in Long Short-Term Memory (LSTM)
  • Chance to work with big brands
  • Higher paycheck

Prerequisites

  • Basic knowledge of Python
  • Well-trained in Machine Learning

Who should take up?

  • Data Science Professionals
  • Analytics Manager
  • E-commerce Professionals
  • Information Architects
  • Software Professionals
  • Business Analysts
  • Analysts with a keen interest to learn Data Science methodologies

Course Content

  • 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
  • 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
  • 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
  • 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
  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • 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
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • 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
  • 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
  • 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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