/Complete-Deep-Learning-with-Projects-Series-

This repository contains everything you need to become proficient in Deep Learning

MIT LicenseMIT

Complete-Deep-Learning-with-Projects-Series-

This repository contains everything you need to become proficient in Deep Learning

Youtube for all the implemented projects and tech interview resources - Ignito Youtube Channel

Complete Cheat Sheet for Tech Interviews - How to prepare efficiently

I took theses Projects Based Courses to Build Industry aligned Data Science and ML skills

Part 1 - How to solve Any ML System Design Problem

Neural Networks

Neural Networks basics

Different types of neural networks

Linear Classifiers

Optimization

Hyper Parameter Tuning

Gradient Descent

Backpropagation Algorithm

Regularization — L2 and dropout regularization

Batch normalization

Build a neural network in Keras

Build a Neural Network With Pytorch

Build a neural network in TensorFlow

Train Neural Networks

Feedforward neural network

Popular Optimization Algorithms

Activation Functions

Strategies for reducing errors

Shallow Neural Networks

Convolutional Neural Networks

Convolution basics and CNN Architectures

Residual networks

Build a Convolutional Network

Batch Normalization and Dropout

Recurrent Neural Networks

RNN Basics

LSTM: Long Short Term Memory Cells

Natural language processing and Word Embeddings

Tensorflow

Tensorflow basics

Tensorflow Playground

Custom Loss Functions

Custom Layers and Models

Callbacks

Distributed Training

Data Pipelines with TensorFlow Data Services

Performance

Autoencoders

Autoencoders Basics

Generative Learning

Generative Adversarial Networks

Generative Adversarial Networks Basics

Useful activation functions and Batch normalization

Transposed convolutions

Generator and Discriminator

Deep Convolutional Generative Adversarial Networks

Implement Generative Adversarial Networks

Attention and Transformers

Attention and Transformers Basics

Sequence to Sequence Models

Attention

Multi-Head Self-Attention

Building Blocks of Transformers

Encoder

Decoder

Parameters Sharing

Build a Transformer Encoder

Graph Neural Networks

Basics of Graphs

Graph Convolutional Networks

Implement — Graph Convolutional Network

Natural Language Processing

Natural Language Processing Basics

Probabilistic Models

Sequence Models

Attention Models

Federated learning