TensorFlow Training Course

TensorFlow Training Course For Beginners

Call Now

Enroll Now

Email Us

TensorFlow Training Course For Beginners Summary

 

TensorFlow Training Course For Beginners is a 4 weeks long Instructor-led and guided training with Practical Hands-On Lab exercises to be taught over 16 hours, 2 sessions per week, 2 hours per session.

  • The medium of instruction is English.
  • All Published Ticket Prices are in US Dollars.

TensorFlow Training Course For Beginners Schedule

 

Please choose from one of the dates in the table below to begin your enrollment :

Dates Weekly Schedule (US Pacific Time)* Price Add to Cart
Dec 6 to Dec 29 Mon/Wed 5:30 PM - 7:30 PM each day $394.00 Add to cart
Dec 7 to Dec 30 Tue/Thu 7:30 AM - 9:30 AM each day $394.00 Add to cart
Jan 8 to Jan 30 Sat/Sun 7:30 AM - 9:30 AM each day $394.00 Add to cart
Jan 10 to Feb 2 Mon/Wed 5:30 PM - 7:30 PM each day $394.00 Add to cart
Jan 11 to Feb 3 Tue/Thu 7:30 AM - 9:30 AM each day $394.00 Add to cart
Feb 5 to Feb 27 Sat/Sun 7:30 AM - 9:30 AM each day $394.00 Add to cart
Feb 7 to Mar 2 Mon/Wed 5:30 PM - 7:30 PM each day $394.00 Add to cart
Feb 8 to Mar 3 Tue/Thu 7:30 AM - 9:30 AM each day $394.00 Add to cart
Mar 14 to Apr 6 Mon/Wed 6:30 PM - 8:30 PM each day $394.00 Add to cart
Mar 15 to Apr 7 Tue/Thu 8:30 AM - 10:30 AM each day $394.00 Add to cart
Mar 19 to Apr 10 Wed/Sun 8:30 AM - 10:30 AM each day $394.00 Add to cart
*click on date/time hyperlink to add your location and find local date/time for first session
Dates and Weekly Schedule (US Pacific Time)* Price
Dec 6 to Dec 29
Mon/Wed 5:30 PM - 7:30 PM each day
$394.00
Enroll
Dec 7 to Dec 30
Tue/Thu 7:30 AM - 9:30 AM each day
$394.00
Enroll
Jan 8 to Jan 30
Sat/Sun 7:30 AM - 9:30 AM each day
$394.00
Enroll
Jan 10 to Feb 2
Mon/Wed 5:30 PM - 7:30 PM each day
$394.00
Enroll
Jan 11 to Feb 3
Tue/Thu 7:30 AM - 9:30 AM each day
$394.00
Enroll
Feb 5 to Feb 27
Sat/Sun 7:30 AM - 9:30 AM each day
$394.00
Enroll
Feb 7 to Mar 2
Mon/Wed 5:30 PM - 7:30 PM each day
$394.00
Enroll
Feb 8 to Mar 3
Tue/Thu 7:30 AM - 9:30 AM each day
$394.00
Enroll
Mar 14 to Apr 6
Mon/Wed 6:30 PM - 8:30 PM each day
$394.00
Enroll
Mar 15 to Apr 7
Tue/Thu 8:30 AM - 10:30 AM each day
$394.00
Enroll
Mar 19 to Apr 10
Wed/Sun 8:30 AM - 10:30 AM each day
$394.00
Enroll
*click on date/time hyperlink to add your location and find local date/time for first session

Course Objectives

 
  • Understand TensorFlow concepts, functions, operations and the execution pipeline.
  • Understand neural networks, deep learning algorithms, and data abstraction layers.
  • Master advanced topics including convolutional neural networks, deep neural networks, recurrent neural networks, and high-level interfaces.
  • Learn how to build deep learning models in TensorFlow and interpret the results.
  • Understand the fundamental concepts of artificial neural networks

Features and Benefits

 
  • 4 weeks, 8 sessions, 16 hours of total Instructor-led and guided
    training
  • Training material, instructor handouts and access to useful
    resources on the cloud provided
  • Practical Hands-on Lab exercises provided
  • Real-life Scenarios

Who should attend ?

 
  • Any working professional who is interested in learning TensorFlow.

Prerequisites

 
  • A prior exposure to data science would be beneficial.

Course Outline

 

1. Introduction to Deep Learning

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • Discuss the idea behind Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go Deep
  • Real-Life use cases of Deep Learning
  • Scenarios where Deep Learning is applicable
  • The Math behind Machine Learning: Linear Algebra
  • The Math Behind Machine Learning: Statistics
  • Review of Machine Learning Algorithms
  • Reinforcement Learning
  • Underfitting and Overfitting
  • Optimization
  • Convex Optimization

2. Fundamentals of Neural Networks

  • Defining Neural Networks
  • The Biological Neuron
  • The Perceptron
  • Multi-Layer Feed-Forward Networks
  • Training Neural Networks
  • Backpropagation Learning
  • Gradient Descent
  • Stochastic Gradient Descent
  • Quasi-Newton Optimization Methods
  • Generative vs Discriminative Models
  • Activation Functions

2. Fundamentals of Neural Networks (Contd.)

  • Loss Functions
  • Loss Function Notation
  • Loss Functions for Regression
  • Loss Functions for Classification
  • Loss Functions for Reconstruction
  • Hyperparameters

3. Fundamentals of Deep Networks

  • Defining Deep Learning
  • Defining Deep Networks
  • Common Architectural Principals of Deep Networks
  • Reinforcement Learning application in Deep Networks
  • Parameters
  • Layers
  • Activation Functions – Sigmoid, Tanh, ReLU
  • Loss Functions
  • Optimization Algorithms
  • Hyperparameters
  • Summary

4. Introduction to TensorFlow

  • What is TensorFlow?
  • Use of TensorFlow in Deep Learning
  • Working of TensorFlow
  • How to install Tensorflow
  • HelloWorld with TensorFlow
  • Running a Machine learning algorithms on TensorFlow

5. Convolutional Neural Networks (CNN)

  • 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

6. Recurrent Neural Networks (RNN)

  • 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

7. Restricted Boltzmann Machine(RBM) and Autoencoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Variational Autoencoders
  • Deep Belief Network

Refund Policy

 
  • 100% refund can be applied if request is initiated 24 hours before the 1st course session.
  • If a class is rescheduled/cancelled by the organizer, registered students will be offered a credit towards any future course or a 100% refund