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Navaneeth Malingan
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README.md
An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning
Knowledge Not Shared is wasted.  Clan Jacobs
This collection is a compilation of Excellent ML and DL Tutorials created by the people below
 Andrej Karpathy blog
 Brandon Roher
 Andrew Trask
 Jay Alammar
 Sebastian Ruder
 Distill
 StatQuest with Josh Starmer
 sentdex
 Lex Fridman
 3Blue1Brown
 Alexander Amini
 The Coding Train
 Christopher Olah
Communities to Follow
 AI Coimbatore Join here🔗⬇️
 Telegram : For Daily Updates
 Facebook : Coimbatore School of AI
 TensorFlow User Group Coimbatore
 Meetup : TFUGCbe
 Facebook : TFUGCbe
This Repo is Created and Maintained by
Why Data Science and how to get started?
 🖥️ HOW TO GET STARTED WITH MACHINE LEARNING!
 How to Build a Meaningful Career in Data Science
 My SelfCreated Artificial Intelligence Masters Degree
 PyImageSearch
 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python
Intro to ML
 Luis Serrano: A Friendly Introduction to Machine Learning
 StatQuest: A Gentle Introduction to Machine Learning
Anyone can do Machine Learning
 Teachable Machine Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.
MOOCs
 Machine Learning by Andrew Ng, Stanford IMDB 10/10 LOL :P
 Datacamp : Data Engineer with Python
 Intro to Machine Learning Topics Covered Naive Bayes, SVM, Decision Trees, Regressions, Outliers, Clustering, Feature Scaling, Text Learning, Feature Selection, PCA, Validation, Evaluation Metrics
 Intro to TensorFlow for Deep Learning The Best Course for Learning TensorFlow
 EndtoEnd Machine Learning
 NVIDIA DEEP LEARNING INSTITUTE
 Introduction to Machine Learning for Coders!
 Practical Deep Learning for Coders, v3
 FastAI
YouTube ML Playlists
Machine Learning Glossary
Machine Learning Fundamentals (These terms will be often used in the below algorithms)
 Bias and Variance
 Cross Validation
 Machine Learning Fundamentals: The Confusion Matrix
 Sensitivity and Specivicity
 ROC and AUC, Clearly Explained!
 StatQuest: Rsquared explained
 StatQuest: P Values, clearly explained
 Regularization Part 1: Ridge Regression
 Regularization Part 2: Lasso Regression
 Maximum Likelihood
 Covariance and Correlation Part 1: Covariance
 Statistics Fundamentals: The Mean, Variance and Standard Deviation
 Statistics Fundamentals: Population Parameters
 Glossary: Statistics
 Glossary: Machine Learning
 Looking at RSquared
Math
 Mathematics for Machine Learning In this post I have compiled great eresources (MOOC, YouTube Lectures, Books) for learning Mathematics for Machine Learning.
 Mathematics for Machine Learning  Book One great book for all things math for machine learning. (free eBook)
 I highly Recommend you to go through the following resources by 3Blue1Brown
 Essence of Linear Algrbra▶️
 Essene of Calculus▶️
 Differential equations▶️
 Gilbert Strang: Linear Algebra vs Calculus▶️
 Basics of Integral Calculus in Tamil▶️
 New fast.ai course: Computational Linear Algebra
 Linear Algebra Book
Python
Numpy
 A Visual Intro to NumPy and Data Representation
 CS231n : Python Numpy Tutorial
 NumPy resources : part of the EndtoEnd Machine Learning library
 100 numpy exercises (with solutions)
 101 NumPy Exercises for Data Analysis (Python)
 Numpy Tutorial – Introduction to ndarray
 SciPy Lectures : NumPy: creating and manipulating numerical data
 Python NumPy Tutorial for Beginners▶️ Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python's Builtin lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more.
 Python NumPy Tutorial – Learn NumPy Arrays With Examples
 Python Numpy Array Tutorial
 NumPy Tutorial: Data analysis with Python
 Deep Learning Prerequisites: The Numpy Stack in Python▶️
Pandas
 A Gentle Visual Intro to Data Analysis in Python Using Pandas
 Data analysis in Python with pandas by Data School▶️
 Best practices with pandas by Data School▶️
 10 minutes to pandas
 Python Pandas Tutorial: A Complete Introduction for Beginners
Machine Learning YouTube Playlists
 CodeBasics: Machine Learning Tutorial Python▶️
 StatQuest: Machine Learning▶️
 sentdex: Machine Learning with Python▶️
 Simplilearn: Machine Learning Tutorial Videos▶️
 Machine Learning Tutorial in Python▶️
 deeplizard: Machine Learning & Deep Learning Fundamentals▶️
ML, DL Visual Explainers
Note: Below you can find the best lectures for popular Machine Learning Algorithms
Linear Regression
 Linear Regression: A friendly introduction by Luis Serrano▶️
 Statistics 101: Linear Regression, The Very Basics▶️
 All Types of Regression📙
Logistic Regression
 Linear Regression vs Logistic Regression  Data Science Training  Edureka▶️
 Logistic Regression and the Perceptron Algorithm: A friendly introduction by Luis Serrano▶️
Decission Tree
 StatQuest: Decision Trees▶️
 StatQuest: Decision Trees, Part 2  Feature Selection and Missing Data▶️
 Decision Tree Introduction with example📙
 Decision Tree📙
 Python  Decision Tree Regression using sklearn📙
 ML  Logistic Regression v/s Decision Tree Classification📙
Random Forest
 StatQuest: Random Forests Part 1  Building, Using and Evaluating▶️
 StatQuest: Random Forests Part 2: Missing data and clustering▶️
 Random Forests for Complete Beginners📙
Boosting Machine Learning
 Boosting Machine Learning Tutorial  Adaptive Boosting, Gradient Boosting, XGBoost  Edureka▶️
 AdaBoost, Clearly Explained▶️
 Gradient Boost Part 1: Regression Main Ideas▶️
 Gradient Boost Part 2: Regression Details▶️
 Gradient Boost Part 3: Classification▶️
 Gradient Boost Part 4: Classification Details▶️
 XGBoost Part1: XGBoost Trees for Regression▶️
 XGBoost Part 2: XGBoost Trees For Classification▶️
 Ensemble methods Scikit learn
SVM
 Support Vector Machines (SVMs): A friendly introduction by Luis Serrano▶️
 Support Vector Machines, Clearly Explained!!! by StatQuest▶️
 Support Vector Machines Part 2: The Polynomial Kernel by StatQuest▶️
 Support Vector Machines Part 3: The Radial (RBF) Kernel by StatQuest▶️
 How Support Vector Machines work / How to open a black box▶️
 Support Vector Machines  The Math of Intelligence (Week 1)▶️
 Demystifying Support Vector Machines📙
 Support Vector Machine (SVM)  Fun and Easy Machine Learning▶️
Bayes Theorem
 Bayes theorem, and making probability intuitive▶️
 A friendly introduction to Bayes Theorem and Hidden Markov Models▶️
 The Bayesian Trap▶️
 Naive Bayes classifier: A friendly approach▶️
KNearest Neighbors
KMeans
 StatQuest: Kmeans clustering▶️
 Machine Learning Tutorial Python  13: K Means Clustering▶️
 K Means Clustering Algorithm  K Means Example in Python  Machine Learning Algorithms  Edureka▶️
Principal Component Analysis (PCA)
 StatQuest: PCA main ideas in only 5 minutes!!!▶️
 StatQuest: Principal Component Analysis (PCA), StepbyStep▶️
 Principal Component Analysis (PCA) by Luis Serrano▶️
Probabilistic Graphical Models
Gradient Descent from Scratch
The Best
 Linear Regression using Gradient Descent📙
 An overview of gradient descent optimization algorithms📙
 Gradient Descent, StepbyStep▶️
 Stochastic Gradient Descent, Clearly Explained!!!▶️
 How Optimization Works A short series on the fundamentals of optimization for machine learning
 Linear Regression using Gradient Descent
 Code
 Polynomial Regression
 Gradient Descent in Linear Regression  Math📙
 Neural Network Backpropagation Basics For Dummies▶️
Extra Good Ones
 3.4: Linear Regression with Gradient Descent  Intelligence and Learning▶️
 3.5: Mathematics of Gradient Descent  Intelligence and Learning▶️
 3.5a: Calculus: Power Rule  Intelligence and Learning▶️
 3.5b: Calculus: Chain Rule  Intelligence and Learning▶️
 3.5c: Calculus: Partial Derivative  Intelligence and Learning▶️
Vanishing Gradient
How to Handle Local Minima
 https://datascience.stackexchange.com/questions/24534/doesgradientdescentalwaysconvergetoanoptimum
 https://datascience.stackexchange.com/questions/18802/doesmlpalwaysfindlocalminimum
 https://www.coursera.org/learn/deepneuralnetwork/lecture/RFANA/theproblemoflocaloptima
Scikitlearn
 An introduction to machine learning with scikitlearn📙
 Python Machine Learning: ScikitLearn Tutorial
Deep Learning
 DEEP BLUEBERRY BOOK This is a tiny and very focused collection of links about deep learning. If you've always wanted to learn deep learning stuff but don't know where to start, you might have stumbled upon the right place!
 6.S191: Introduction to Deep Learning (2019)
 Class Lectures (YouTube)  MIT 6.S191: Introduction to Deep Learning
 Lab
 MIT 6.S191 Introduction to Deep Learning (2020)
 MIT Deep Learning Basics: Introduction and Overview
 MIT Deep Learning by Lex Fridman
 Deep Learning Lectures (YouTube)
 Deep Learning in Tamil
Deep Leraning Books
 The Deep Learning Textbook from Ian Goodfellow, Yoshua Bengio, and Aaron Courville
 Neural Networks And Deep Learning by Michael Nielsen
 Grokking Deep Learning by Andrew Trask
Deep Lerning Papers
NN
 A friendly introduction to Deep Learning and Neural Networks▶️
 Machine Learning for Beginners: An Introduction to Neural Networks📙 A simple explanation of how they work and how to implement one from scratch in Python.
 A Visual and Interactive Guide to the Basics of Neural Networks📙
 A Visual And Interactive Look at Basic Neural Network Math📙
 Neural Network Architectures▶️
 Neural Networks Demystified by Welch Labs▶️
 Supporting code for short YouTube series Neural Networks Demystified.
 Neural networks Series by 3Blue1Brows▶️
Computer Vision
 CS131 Computer Vision: Foundations and Applications Fall 2019
 CS231A: Computer Vision, From 3D Reconstruction to Recognition Winter 2018
 CS231n Convolutional Neural Networks for Visual Recognition
CNN
 CS231n: Convolutional Neural Networks for Visual Recognition Spring 2019
 CS231n: Convolutional Neural Networks for Visual Recognition
 A friendly introduction to Convolutional Neural Networks and Image Recognition
 A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way
 Convolutional Neural Networks for Beginners
 Tensorflow Convolutional Neural Network (CNN)
 Convolutional Networks Book
 CNNs, Part 1: An Introduction to Convolutional Neural Networks
 CS231n Winter 2016 BY Andrej Karpathy 15 Videos
 Intuitive understanding of 1D, 2D, and 3D Convolutions in Convolutional Neural Networks
 CNN ExplainerAn interactive visualization system designed to help nonexperts learn about Convolutional Neural Networks (CNNs)
Object Detection
 SIFT  Scale Invariant Feature Transform
 Hog Intuition  Histogram of Oriented Gradients
 NMS  Non Max Suppression
 Object Localization  Bounding Box Regression
 Object Detection
 RCNN
 Spatial Pyramid Matching  SPM
 SPPNet Object Detection
 Fast RCNN Network
Yolo v4 Object Detection  How it Works & Why it's So Amazing!
 Frameworks and Libraries
 Detectron2 by Facebook AI
 MMDetection
 MediaPipe
 YOLO
 TensorFlow Object Detection API
 Labeling Tools
 LabelImg
 Roboflow
 Code samples
YOLOObjectCountingAPI ### Image Segmentation
Computer Vision Tutorial: A StepbyStep Introduction to Image Segmentation Techniques (Part 1)
GANs
 A Friendly Introduction to Generative Adversarial Networks (GANs) by Luis Serrano
 Generative Adversarial Networks (GANs) by Ahlad Kumar
 Building our first simple GAN
 Face editing with Generative Adversarial Networks
 Variational Autoencoders
 Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
 Generative Models
Style Transfer
NLP
 CS224n: Natural Language Processing with Deep Learning
 NLP and The Reformer
 The Illustrated Word2vec
RNN
 Illustrated Guide to Recurrent Neural Networks: Understanding the Intuition
 Anyone Can Learn To Code an LSTMRNN in Python (Part 1: RNN) Baby steps to your neural network's first memories.
 The Unreasonable Effectiveness of Recurrent Neural Networks
 An Introduction to Recurrent Neural Networks for Beginners A simple walkthrough of what RNNs are, how they work, and how to build one from scratch in Python.
 Attention and Augmented Recurrent Neural Networks by Distill
 Visualizing memorization in RNNs by Distill Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use shortterm or longterm contextual understanding.
 Deep Learning for NLP: ANNs, RNNs and LSTMs explained!
LSTM
 Understanding LSTM Networks
 LSTM implementation explained
 A Gentle Introduction to LSTM Autoencoders
 Keras LSTM tutorial – How to easily build a powerful deep learning language model
Transformers and Self Attention
Visual Guide to Transformer Neural Networks (Highly Recommended)
 Part 1  Position Embeddings
 Part 2  MultiHead & Self Attention
 Part 3  Decoder’s Masked Attention
 NLP Transformers Attention Playlist
 The Illustrated Transformer
 The Annotated Transformer
 Transformers Paper and Code
 Transformers from Scratch
 Transformers Notes
 Transformers, Explained: Understand the Model Behind GPT3, BERT, and T5
 A comprehensive overview of Transformer variants.
 How to become an NLP & Transformer Model Guru
 A Visual Guide to Using BERT for the First Time
 The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
 BERT Explained: State of the art language model for NLP
 BERT – State of the Art Language Model for NLP
GPT
Reinforcement Learning
 Deep Reinforcement Learning Course 🕹️ A Free course in Deep Reinforcement Learning from beginner to expert.
 Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
 Unity Machine Learning Agents Toolkit
 🖥️ WRITING MY FIRST MACHINE LEARNING GAME! (1/4)
 Deep Reinforcement Learning: Pong from Pixels by Andrej Karpathy
 A Beginner's Guide to Deep Reinforcement Learning
 An Introduction to Unity MLAgents
 Deep Reinforcement Learning Algorithms with PyTorch
 LECTURES: Introduction to Reinforcement Learning  David Silver
 BOOK: Reinforcement Learning  An Introduction by Sutton and Barto
 BOOK: Deep Reinforcement Learning Hands On by Maxim Lapan
PyTorch
 Udacity : Deep Learning with PyTorch
 Deep Learning (PyTorch) : Code
 Udacity : Secure AI
 TORCHSCRIPT
 PyTorchZeroToAll (in English) Sung Kim a Series of 14 Videos
 Supporting Code
 Slides
TensorFlow
 Introduction to TensorFlow 2.0: Easier for beginners, and more powerful for experts (TF World '19)
 TensorFlow Lite: Solution for running ML ondevice (TF World '19)
 Machine Learning in JavaScript (TensorFlow Dev Summit 2018)
 TensorFlow.js Quick Start
 Keras vs. tf.keras: What’s the difference in TensorFlow 2.0?
 How To Run TensorFlow Lite on Raspberry Pi for Object Detection
 How computers learn to recognize objects instantly  Joseph Redmon
TensorFlow Courses
 Intro to TensorFlow for Deep Learning
 TensorFlow in Practice Specialization : Coursera
 TensorFlow: Data and Deployment Specialization : Coursera
PyTorch Vs TensorFlow
Transfer Learning
 Transfer Learning with Keras and Deep Learning
 A Comprehensive Handson Guide to Transfer Learning with RealWorld Applications in Deep Learning
 TensorFlow Core Tutorials
Deploy Models
 Machine Learning in 5 Minutes: How to deploy a ML model (SurveyMonkey Engineer explains)
 Deploy Machine Learning Models with Django
 MlFlow  An open source platform for the machine learning lifecycle
 TensorFlow: Data and Deployment Specialization
MlOps
CheatSheets
 CHRIS ALBON Cheat Sheets and Flash Cards
 DataScienceCheatSheet
 MLOps Tooling Landscape v2 (+84 new tools)  Dec '20
GPU
Edge ML Kits
Data Science Competitions
Important Youtube🎬 Channels in the field of AI/ML/RL/DS
 3Blue1Brown
 StatQuest with Josh Starmer
 Sentdex
 Luis Serrano
 Brandon Rohrer
 deeplizard
 Tech With Tim
 Microsoft Research
 Corey Schafer
 Data School
 Two Minute Papers
 Welch Labs
 Simplilearn
 Great Learning
 DeepLearning.TV
 TensorFlow
 Deeplearning.ai
 Code Bullet
 edureka!
 Lex Fridman
 The Artificial Intelligence Channe
 freeCodeCamp.org
 CloudxLab
 Alexander Amini
 Jeff Heaton
 Abhishek Thakur
 The Coding Train
Reference
 🖥️ HOW TO GET STARTED WITH MACHINE LEARNING!
 My SelfCreated Artificial Intelligence Masters Degree
 https://endtoendmachinelearning.teachable.com/courses/667372/lectures/11900568
 ML Fundamentals by StatQuest
 Machine Learning with Python by sentdex
 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python  Daniel Bourke
 Data School
 Neural Networks and Deep Learning
 https://www.machinelearningisfun.com/
 https://medium.com/@ageitgey/machinelearningisfun80ea3ec3c471
 https://medium.com/greyatom
 https://greyatom.com/glabs
 John Searle: "Consciousness in Artificial Intelligence"  Talks at Google
 ML Terms
 https://github.com/natureofcode/NOCS172IntelligenceLearning/tree/master/week3classificationregression
 https://github.com/natureofcode/NOCS172IntelligenceLearning