Deep Learning Training

Lesson 01 - Course Introduction

Introductions to Deep Learning

Lesson 02 - AI and Deep learning introduction

What is AI and Deep Learning

Brief History of AI

Recap: SL, UL and RL

Deep Learning: Successes Last Decade

Demo and Discussion: Self-Driving Car Object Detection

Applications of Deep Learning

Challenges of Deep Learning

Demo and Discussion: Sentiment Analysis Using LSTM

Full Cycle of a Deep Learning Project

Key Takeaways

Knowledge Check

Lesson 03 - Artificial Neural Network

Biological Neuron Vs Perceptron

Shallow Neural Network

Training a Perceptron

Demo Code #1: Perceptron (Linear Classification)

Backpropagation

Role of Activation Functions and Backpropagation Demo

Code #2: Activation Function Demo Code #3: Backprop Illustration

Optimization

Regularization

Dropout layer

Demo Code #4: Dropout Illustration, Lesson-end Exercise (Classification Kaggle Dataset)

Key Takeaways

Knowledge Check

Lesson-end Project

Lesson 04 - Deep Neural Network & Tools

Deep Neural Network: Why and Applications

Designing a Deep Neural Network

How to Choose Your Loss Function?

Tools for Deep Learning Models

Keras and its Elements

Demo Code #5: Build a Deep Learning Model Using Keras Tensorflow and Its Ecosystem

Demo Code #6: Build a Deep Learning Model Using Tensorflow TFlearn

Pytorch and its Elements

Demo Code #7: Build a Deep Learning Model Using Pytorch

Demo Code #8: Lesson-end Exercise

Key Takeaways

Knowledge Check

Lesson-end Project

Lesson 05 - Deep Neural Net optimization, tuning, interpretability

Optimization Algorithms

SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam Demo code #9: MNIST Dataset Batch Normalization

Demo Code #10

Exploding and Vanishing Gradients

Hyperparameter Tuning

Demo Code #11

Interpretability

Demo Code#12: MNIST– Lesson-end Project with Interpretability Lessons

Width vs Depth

Key Takeaways

Knowledge Check

Lesson-end Project

Lesson 06 - Convolutional Neural Net

Success and History

CNN Network Design and Architecture

Demo Code #13: Keras

Demo Code #14: Two Image Type Classification (Kaggle), Using Keras

Deep Convolutional Models

Key Takeaways

Knowledge Check

Lesson-end Project

Lesson 07 - Recurrent Neural Networks

Sequence Data

Sense of Time

RNN Introduction

Demo Code #15: Share Price Prediction with RNN

LSTM (Retail Sales Dataset Kaggle)

Demo Code #16: Word Embedding and LSTM

Demo Code #17: Sentiment Analysis (Movie Review)

GRUs

LSTM vs GRUs

li>Demo Code #18: Movie Review (Kaggle), Lesson-end Project)

Key Takeaways

Knowledge Check

Lesson-end Project

Lesson 08 - Autoencoders

Introduction to Autoencoders

Applications of Autoencoders

Autoencoder for Anomaly Detection

Demo Code #19: Autoencoder Model for MNIST Data

Key Takeaways

Knowledge Check

Lesson-end Project