Deep Learning

Welcome to our Deep Learning Course! This comprehensive course aims to equip you with the knowledge and skills required to understand and implement various deep learning architectures.

Course Overview

Introduction

Begin your deep learning journey, understanding its significance and applications.

Neural Networks

Learn the basics of neural networks and how they work.

Loss Functions

Understand the different types of loss functions used in deep learning.

Activation Functions

Explore the various activation functions and their importance.

Weight Initialization

Learn techniques for initializing weights in neural networks.

Vanishing & Exploding Gradients

Delve into the problems of vanishing and exploding gradients and how to mitigate them.

Feedforward Neural Networks

Study the architecture and applications of feedforward neural networks.

Convolutional Neural Networks

Learn about CNNs and their use in image and video recognition tasks.

Recurrent Neural Networks

Explore RNNs and their applications in sequence modeling.

Transformers

Understand the transformer architecture and its impact on NLP.

Generative Adversarial Networks

Learn about GANs and how they can generate new data that is similar to your input data.

Getting Started

To make the most out of this course, it's highly recommended to actively participate and experiment with deep learning models. Let's dive in and unravel the fascinating world of deep learning!