Deep Learning, Neural Networks and TensorFlow

Preference Dates Timing Delivery Method
Evening Course 11, 12, 14, 15, 18, 19 October 2020 07:00PM - 09:30PM Webinars & Hands-on Projects

Course Description

If you want to break into AI, this course will help you do so. Deep Learning is one of the most highly sought after skills in the IT industry.

This course will teach you the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from health-care, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow framework.

Unit 1 – Environment Setup

Setting up a GPU on the Cloud 
Installing and Configuring Python and TensorFlow on the GPU


Unit 2 – Introduction to Deep Learning

  • What is Deep Learning
  • Trends driving the rise of Deep Learning
  • Practical Applications of Deep Learning
  • What is a Neural Network?
  • Supervised Learning with Neural Networks
  • Why is Deep Learning taking off?

Unit 3 – Data Handling for Deep Learning

  • Data Preparation
  • Data Import and Storage
  • Data Exploration
  • Data Standardization and Normalization
  • Practical Project: Reading Data from H5 Files

Unit 4 – Neural Networks Basics

  • Artificial Neural Network (ANN) Structure
  • Biological Neurons and Artificial Neurons
  • Model Representation
  • Forward Propagation
  • Activation Functions
  • Cost and Loss Functions
  • Back Propagation and Gradient Descent
  • Practical Project: Building a Neural Network from Scratch

Unit 5 – TensorFlow Basics

  • Introduction to TensorFlow
  • TensorFlow Basic Syntax
  • TensorFlow Graphs
  • Variables and Placeholders
  • TensorFlow Regression Example
  • TensorFlow Classification Example
  • Saving and Restoring Models
  • Practical Project: Developing a Classifier for MNIST Dataset with TensorFlow

Unit 6 – Tuning and Optimizing Neural Networks

  • Train / Dev / Test Sets
  • Regularization (L2 Regularization, Dropout)
  • Normalizing Input
  • Weight Initialization
  • Mini-batch Gradient Descent
  • Practical Project: Developing a Deep Neural Network for Image Classification

Unit 7 – Convolutional Neural Networks

  • Introduction to Computer Vision
  • Convolution Operation and Edge Detection
  • Padding
  • Strides
  • Pooling Layers
  • Template for building a Convolutional Neural Networks
  • Overview of CNN Architectures
  • Practical Project: Developing a CNN Model for Image Classification

Unit 8 – Recurrent Neural Networks

  • RNN Key Concepts
  • Manual Creation of RNN
  • Vanishing Gradients
  • LSTM and GRU
  • RNN with TensorFlow
  • Practical Project: Predicting Time Series with RNN
  • Professionals or students who are interested in deep learning, and already have Python Programming and machine learning experience.
  • Future Data Science Professionals and Engineers
  • Intermediate Python programmers interested in enhancing their existing skills.

Python Programming Experience
Python for Data Science and Machine Learning

The participants who have successfully completed this course will be able to develop deep learning applications: including natural language processing and image recognition.