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 April 15, 2021 - May 11, 2021 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 Course Schedule

• April 15, 2021 - May 11, 2021 US Pacific time
• 16 Hours | 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 city name and check your local date and time for the first session to be held on April 15, 2021 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