Deep Learning

Why Deep Learning nowaday?

Getting Started to dig deep in ML fundamental concepts

How to use Simple Network to make a prediction/neural network

How to evaluate the predictions in neural network

Identification Errors to help train models

Three Paradiam Concepts : Predict, Compare, Learn

Deep Neural network and code

Work on the 10,000-foot view of neural networks

Introduction : Overfitting, Dropout, and batch gradient descent

Modeling probabilities and activation functions

Introduction : Convolutional neural networks and the usability of structure to counter overfitting

Deep dives into NLP (Natural language processing)

Recurrent neural networks/A state-of-the-art approach

Fast-track, building a deep learning framework

Challenge 'Language modeling' using recurrent neural network

Basic Privacy concepts : federated learning, homomorphic encryption, and secure multiparty computation

Tools and resources in deep learning journey