DEEP_LEARNING.md

Deep Learning

Deep Learning is an Artificial Intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. DL is a subset of ML in AI that has networks capable of learning unsupervised from data that is unstructured or unlabeled, also known as deep neural learning or deep neural network.

Complete Courses

• For learning deep, advanced concepts of DL and becoming an expert, you can go ahead with this paid course on Udemy.

• TensorFlow for Beginners

• CNN for Computer Vision with Keras and TensorFlow in Python

• Course by Yann Lecunn on Deep Learning taught at NYU

• Course Specifically designed for DEEP LEARNING FOR NLP

• PyTorch for Beginners

Videos lectures

• This lecture series has very good introduction to Neural Network and Deep Learning

• The concepts in this series explained are bit abstract, concepts are hard to understand in first go

• Deep Learning Lectures from Oxford University

• Deep Learning for Vision by Stanford

• Video Lectures By Yoshua Bengio on Theoritical Aspects of Deep Learning

• Video Lectures by the GodFather Geoffrey Hinton on introduction to Deep Learning and some advanced concepts too.

• Intro to Energy based model by Yann Lecunn.

• Yoshua Bengio's lecture series recorded in KDD' 14

• Ronan Collobert lecture (it's quite old new, from 2008 but I think it is still useful)

• Lecture series by Chris Manning and Richard Socher given at NAACL 2013

• Lecture series for DL & NLP with some practical guidelines

• TOPIC COURSE IN DEEP LEARNING by Joan Brune, UC Berkley Stats Department

Blogs & Reading material

• Reading resources

• Reading list by Hinton

• Short notes on backpropagation and word embeddings

• Blogpost on some DL applications

• Some useful tricks for training Neural Networks

• NLP using Torch

• This is a short book on Deep Learning written by Yoshua Bengio

• This is holy bible on Deep Learning. This web page has a book draft written by Yoshua Bengio and Ian Goodfellow. Later person is author of Theano library.

• ENSEMBLING guide. It is Very useful for designing practical ML systems

• Tensorflow.js Train and Deploy machine learning models in the browser.

• GitHub Copilot

• PyTorch. Efficient Framework for implementing Neural Networks

• Deep Learning Tutorial - Scaler Topics

Diving Deep

Topics to Cover

* Keras.
* Nltk.
* All methods and techniques mentioned are listed below:

Deep Learning and NLP 🔥

1. Keras

• All about Keras
• A friendly guide to Keras
• Sequential
  • Guide To Sequential Model
  • How To Build Multi-Layer Perceptron Neural Network Models with Keras
  • Getting started with Sequential model Video
• Dense
  • The Dense Layer
  • Understand The Dense Layer
  • Video on Dense Layer
• Adam
  • Intro to Adam
  • What is Adam
• KerasClassifier
• Project to try out: Rock Paper Scissors game

2. Nltk

• What is Nltk
• word_tokenize, sent_tokenize
  • Why Tokenize
  • Tokenize
• stopwords
  • What are Stopwords and Examples
  • How to implement StopWords
• PunktSentenceTokenizer
  • PunktSentenceTokenizer
• Stemming and Lemmatization
  • What is Stemming
  • What is Lemmatization
  • Stemming and Lemmatization
• Project to try out: Chat Bot