This event has been UPDATED since it was first published. View the UPDATED & Detailed TensorFlow Training course for beginners Information here.

16 Hours TensorFlow Training course is being delivered from October 22, 2020 - November 17, 2020 US Pacific Time for 16 hours over 4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.

• All Published Ticket Prices are in US Dollars
• The course will be taught in English language

16 Hours TensorFlow Training Schedule

• October 22, 2020 - November 17, 2020 US Pacific time
• 4 Weeks | 2 hours on Tuesdays, 2 hours on Thursdays every week US Pacific time
• 8:30 AM - 10:30 AM US Pacific time each of those days
• Please click here to add your location and check your local date and time for first session to be held on October 22, 2020 at 8:30 AM US Pacific Time.

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

Prerequisites

• It is recommended that participants are familiar with programming (preferably in Python), along with familiarity with statistics, algebra, and probability.
• A prior exposure to data science would be beneficial.

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

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
• 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