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Table of Contents

Resources in Large Language Models

• [X] Llama 2
• [ ] Getting Started Guide - Llama 2
• [ ] GitHub - Llama 2
• [ ] Github - LLama 2 Recipes
• [ ] Research Paper
• [ ] Model Card
• [ ] Responsible Use Guide
• [ ] Acceptable Use Policy
• [ ] Replicate
• [ ] LangChain
• [X] NeurIPS Large Language Model Efficiency Challenge

Must Read Papers in Computer Vision

• [X] U-Net: Convolutional Networks for Biomedical Image Segmentation

Learn Philosophy

• [X] The Development Of Neural Networks
• [X] Receptive Field in CNN
• [ ] Standard Gaussian Distribution - Modelling Nature
• [ ] Convolution - Math Driving The Computer Vision
• [ ] Half Order Derivatives
• [ ] Fourier Transforms For Image Processing
• [X] Singular Value Decomposition — Diagnolization of Square Matrix
• [ ] Can you find Inverse of Rectangular Matrix? YES, Go through this
• [ ] Intuitively Understanding Convolutions for Deep Learning
• [ ] Image Segmentation - Basics From TensorFlow
• [ ] UNet — Line by Line Explanation
• [ ] U-Net: Convolutional Networks for Biomedical Image Segmentation
• [ ] ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing
• [ ] Deep CNN for Removal of Salt and Pepper Noise
• [ ] A noise-robust convolutional neural network for image classification
• [ ] Xception: Deep Learning with Depthwise Separable Convolutions
• [ ] Evidence Lower Bound (ELBO)
• [X] Evidence, KL-divergence, and ELBO
• [X] Bayesin Linear Regression

Pre-Trained Deep Neural Networks For Transfer Learning

• [ ] Advanced Guide to Inception v3
• [ ] Inception V3 - Keras Blog
• [ ] Deep Residual Learning for Image Recognition

Resources — Popular Modern & Traditional Machine Learning Algorithms — Theory — Math — Implementation

• [X] Machine Learning Cheatsheet — be used to with ML terms
• [X] Deep Learning Book
• [X] Basic Image Processing — learn basics of image processing for image-preprocessing.
• [X] Xgboost with Different Categorical Encoding Methods
• [X] Linear Regression | Lasso Regression | Ridge Regession — details of regression concepts with thoery and code.
• [X] Magic Behind, Gaussian Naive Bias Classification Algorithm
• [X] The Theory and Code Behind K-Nearest Neighbors
• [X] Learn About Decision Trees — Working and Methods in Layman's Term With Code
• [X] Get Used With Logistic Regression — With Code and Math Running Behind This Algorithm
• [X] Various Kinds of Distances in Data Mining and Machine Learning
• [X] Bayes' Theorem

Blogs — Be Better Everyday

Base of Modern Machine Learning

The Essense of Linear Algebra - 3 Blue 1 Brown

• [X] Chapter I Vectors
• [ ] Chapter II Linear combinations, span, and basis vectors
• [ ] Chapter III Linear transformations and matrices
• [ ] Chapter IV Matrix multiplication as composition
• [ ] Chapter V Three-dimensional linear transformations
• [ ] Chapter VI The determinant
• [ ] Chapter VII Inverse matrices, column space and null space
• [ ] Chapter VIII Nonsquare matrices as transformations between dimensions
• [ ] Chapter IX Dot products and duality
• [ ] Chapter X Cross products
• [ ] Chapter XI Cross products in the light of linear transformations
• [ ] Chapter XII Cramer's rule, explained geometrically
• [ ] Chapter XIII Change of basis
• [ ] Chapter XIV Eigenvectors and eigenvalues
• [ ] Chapter XV A quick trick for computing eigenvalues
• [ ] Chapter XVI Abstract vector spaces

Essense of Calculus - 3 Blue 1 Brown

• [ ] Chapter I The Essense of Calculus
• [ ] Chapter II The paradox of the derivative

Various Useful Mathematical Transformations

• [ ] Radon Transformation
• [ ] Fourier Transform
• [ ] Hankel Transformation
• [ ] Cross Correlation - Generalized Projection of Function Into Reference Vector
• [ ] Autocorrelation
• [ ] Convolution
• [ ] Correlation
• [ ] Laplace Transformation
• [ ] Kullback–Leibler Divergence

Deep Learning

• [X] Creating Neural Network From Scratch — Step By Step With Pythonic Code
• [X] Learn About Bayesian Deep Learning
• [X] Learn Neural Networks and Deep Learning From Scratch — Theory
• [X] Learn BERT — Bidirectional Encoder Representations from Transformers — state-of-art NLP model
• [X] Generative Pre-trained Transformer 3 (GPT-3) — revolutionary NLP model — 515 times more powerful than BERT
• [X] XGBoost Tutorials — Docs from the creater themselves

Machine Learning In Production

• [X] ML Ops: Machine Learning as an Engineering Discipline
• [X] Rules of Machine Learning : Best Practices for ML Engineering

Regular Expressions — Irksome, Yet Useful

• [X] Regular Expression — Official Python Regex Module
• [X] Learn Regex
• [X] Regex Made Easy With Real Python
• [ ] Regular Expressions Demystified

Numpy — Numerical Python

• [X] Numpy Basics
• [X] Numpy Fundamentals

Core Python

• [X] Dive Into Python
• [X] Learn About Python's Pathlib — No Really, Python's Pathlib is Great
• [X] Python 101
• [X] Object Oriented With Python — Wholesome Blog For Learning OOP with Python 3
• [X] Code Refactoring for Software Engineering
• [X] Guide to Python Design Patterns
• [X] Popular Python Design Patterns - Explicitely Python
• [X] Learn Python By Doing Python

Be Pythonic

• [X] Writing Pythonic Code — Transforming from messy code to beautiful pythonic code
• [X] Write More Pythonic Code
• [X] PEP 8 -- Style Guide for Python Code
• [X] The Hitchhiker’s Guide to Python!

Research and Experiment Tools : NOTEBOOK

• [ ] Article: Securely storing configuration credentials in a Jupyter Notebook
• [ ] Article: Automatically Reload Modules with %autoreload
• [ ] Calmcode: ipywidgets
• [ ] Documentation: Jupyter Lab
• [ ] Pluralsight: Getting Started with Jupyter Notebook and Python
• [ ] Youtube: William Horton - A Brief History of Jupyter Notebooks
• [ ] Youtube: I Like Notebooks
• [ ] Youtube: I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
• [ ] Youtube: Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
• [ ] Youtube: nbdev live coding with Hamel Husain
• [ ] Youtube: How to Use JupyterLab

Modern Machine Learning With Scikit-Learn

• [X] Article: Stacking made easy with Sklearn
• [X] Article: Curve Fitting With Python
• [X] Article: A Guide to Calibration Plots in Python
• [X] Calmcode: human-learn
• [X] Datacamp: Supervised Learning with scikit-learn
• [X] Datacamp: Machine Learning with Tree-Based Models in Python
• [X] Datacamp: Introduction to Linear Modeling in Python
• [X] Datacamp: Linear Classifiers in Python
• [X] Datacamp: Generalized Linear Models in Python
• [X] Notebook: scikit-learn tips
• [X] Pluralsight: Building Machine Learning Models in Python with scikit-learn
• [X] Video: human learn
• [X] Youtube: dabl: Automatic Machine Learning with a Human in the Loop 00:25:43
• [X] Youtube: Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python
• [X] Youtube: DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1

Pandas — Be Able to Manipulate Data

• [X] Learn Basic EDA with Pandas
• [X] Article: Modern Pandas
  • [X] Modern Pandas | PART 1 | Introduction
  • [X] Modern Pandas | PART 2 | Method Chaining
  • [X] Modern Pandas | PART 3 | Indexes
  • [X] Modern Pandas | PART 4 | Performance
  • [X] Modern Pandas | PART 5 | Tidy
  • [X] Modern Pandas | PART 6 | Visualization
  • [X] Modern Pandas | PART 7 | Time Series
  • [X] Modern Pandas | PART 8 | Scaling

Hands On Tensorflow and Keras

• [X] Coursera: Introduction to Tensorflow
• [X] Coursera: Convolutional Neural Networks in TensorFlow
• [X] Deeplizard: Keras - Python Deep Learning Neural Network API
• [X] Book: Deep Learning with Python (Page: 276)
• [X] Datacamp: Deep Learning in Python
• [X] Datacamp: Convolutional Neural Networks for Image Processing
• [X] Datacamp: Introduction to TensorFlow in Python
• [X] Datacamp: Introduction to Deep Learning with Keras
• [X] Datacamp: Advanced Deep Learning with Keras
• [X] Google: Machine Learning Crash Course
• [X] Pluralsight: Deep Learning with Keras
• [X] Udacity: Intro to TensorFlow for Deep Learning

Control Your Code — Versioning

• [X] Version Control Via Git
• [X] A Sucessful Git Branching Model
• [X] Git & Github Crash Course
• [X] Everything About Git & Gitbash

Developer Tools For ML

• Weight And Biases

Dive Into Deep learning

• [X] Introduction
• [X] Preliminaries

Convolutional Neural Networks

• [X] Lecture 1 | Introduction to Convolutional Neural Networks
• [X] Lecture 2 | Image Classification
• [X] Lecture 3 | Loss Functions and Optimizations
• [X] Lecture 4 | Introduction to Neural Networks
• [X] Lecture 5 | Convolutional Neural Networks
• [X] Lecture 6 | Training Neural Networks I
• [X] Lecture 7 | Training Neural Networks II
• [X] Lecture 8 | Deep Learning Software
• [X] Lecture 9 | CNN Architectures
• [X] Lecture 10 | Recurrent Neural Networks
• [X] Lecture 11 | Detection and Segmentation
• [X] Lecture 12 | Visualizing and Understanding
• [X] Lecture 13 | Generative Models
• [X] Lecture 14 | Deep Reinforcement Learning
• [X] Lecture 15 | Efficient Methods and Hardware for Deep Learning
• [X] Lecture 16 | Adversarial Examples and Adversarial Training

Master The Computer Vision — List of blogs and tutorials for diving deep into world of intelligent vision

Beginner Level — Mathematics

• [X] Linear Algebra
• [X] Singular Value Decomposition
• [X] Basic Pattern Recognition
• [X] Reduce The Dimesnion — PCA
• [X] Guide To Kalman Filtering
• [X] Fourtier Transforms

Beginner Level — Image Procesing

• [X] Online Course offered by Duke University on Coursera
• [X] Image Processing With Scipy and Numpy

Advanced Level

• [X] Linear Discriminant Analysis
• [X] Probability, Bayes rule, Maximum Likelihood, MAP
• [X] Mixtures and Expectation-Maximization Algorithm
• [X] Introductory level Statistical Learning
• [X] Hidden Markov Models
• [X] Support Vector Machines
• [X] Genetic Algorithms
• [X] Bayesian Networks

First Principles Of Computer Vision

• [X] Topics Covered | Introduction
• [X] A Brief History of Imaging | Image Sensing

Theory Of Classical Machine Learning

• [ ] StatQuest: Machine Learning
  • [X] StatQuest: Fitting a line to data, aka least squares, aka linear regression. 0:09:21
  • [X] StatQuest: Linear Models Pt.1 - Linear Regression 0:27:26
  • [X] StatQuest: StatQuest: Linear Models Pt.2 - t-tests and ANOVA 0:11:37
  • [X] StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
  • [X] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
  • [X] StatQuest: Logistic Regression 0:08:47
  • [X] Logistic Regression Details Pt1: Coefficients 0:19:02
  • [X] Logistic Regression Details Pt 2: Maximum Likelihood 0:10:23
  • [X] Logistic Regression Details Pt 3: R-squared and p-value 0:15:25
  • [X] Saturated Models and Deviance 0:18:39
  • [X] Deviance Residuals 0:06:18
  • [X] Regularization Part 1: Ridge (L2) Regression 0:20:26
  • [X] Regularization Part 2: Lasso (L1) Regression 0:08:19
  • [X] Ridge vs Lasso Regression, Visualized!!! 0:09:05
  • [X] Regularization Part 3: Elastic Net Regression 0:05:19
  • [X] StatQuest: Principal Component Analysis (PCA), Step-by-Step 0:21:57
  • [X] StatQuest: PCA main ideas in only 5 minutes!!! 0:06:04
  • [X] StatQuest: PCA - Practical Tips 0:08:19
  • [X] StatQuest: PCA in Python 0:11:37
  • [X] StatQuest: Linear Discriminant Analysis (LDA) clearly explained. 0:15:12
  • [X] StatQuest: MDS and PCoA 0:08:18
  • [ ] StatQuest: t-SNE, Clearly Explained 0:11:47
  • [X] StatQuest: Hierarchical Clustering 0:11:19
  • [X] StatQuest: K-means clustering 0:08:57
  • [X] StatQuest: K-nearest neighbors, Clearly Explained 0:05:30
  • [X] Naive Bayes, Clearly Explained!!! 0:15:12
  • [ ] Gaussian Naive Bayes, Clearly Explained!!! 0:09:41
  • [X] StatQuest: Decision Trees 0:17:22
  • [X] StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data 0:05:16
  • [X] Regression Trees, Clearly Explained!!! 0:22:33
  • [ ] How to Prune Regression Trees, Clearly Explained!!! 0:16:15
  • [X] StatQuest: Random Forests Part 1 - Building, Using and Evaluating 0:09:54
  • [X] StatQuest: Random Forests Part 2: Missing data and clustering 0:11:53
  • [X] The Chain Rule 0:18:23
  • [X] Gradient Descent, Step-by-Step 0:23:54
  • [X] Stochastic Gradient Descent, Clearly Explained!!! 0:10:53
  • [X] AdaBoost, Clearly Explained 0:20:54
  • [X] Gradient Boost Part 1: Regression Main Ideas 0:15:52
  • [X] Gradient Boost Part 2: Regression Details 0:26:45
  • [X] Gradient Boost Part 3: Classification 0:17:02
  • [X] Gradient Boost Part 4: Classification Details 0:36:59
  • [X] Support Vector Machines, Clearly Explained!!! 0:20:32
  • [X] Support Vector Machines Part 2: The Polynomial Kernel 0:07:15
  • [X] Support Vector Machines Part 3: The Radial (RBF) Kernel 0:15:52
  • [X] XGBoost Part 1: Regression 0:25:46
  • [X] XGBoost Part 2: Classification 0:25:17
  • [X] XGBoost Part 3: Mathematical Details 0:27:24
  • [X] XGBoost Part 4: Crazy Cool Optimizations 0:24:27
  • [X] StatQuest: Fiitting a curve to data, aka lowess, aka loess 0:10:10
  • [X] Statistics Fundamentals: Population Parameters 0:14:31
  • [X] Principal Component Analysis (PCA) clearly explained (2015) 0:20:16
  • [X] Decision Trees in Python from Start to Finish 1:06:23

Video Tutorials — Deep Learning

• [ ] MIT 6S191 | Deep Learning Lectures

  • [X] Lecture 1 | Indroduction to Deep Leaning
  • [X] Lecture 2 | Recurrent Neural Networks
  • [X] lecture 3 | Convolutional Neural Network
  • [X] Lexture 4 | Deep Generative Models
  • [X] Lecture 5 | Reinforcement Learning

• [ ] Machine Learning Engineering for Production (MLOps) Specialization — COURSERA SPECIALIZATION

  • [X] Course I | Introduction to Machine Learning in Production
  • [X] Course II | Machine Learning Data Lifecycle in Production
  • [X] Course III | Machine Learning Modeling Pipelines in Production
  • [X] Course IV | Deploying Machine Learning Models in Production

• [X] MIT: 18.06 Linear Algebra (Professor Strang)

  • [X] 1. The Geometry of Linear Equations 0:39:49
  • [X] 2. Elimination with Matrices. 0:47:41
  • [X] 3. Multiplication and Inverse Matrices 0:46:48
  • [X] 4. Factorization into A = LU 0:48:05
  • [X] 5. Transposes, Permutations, Spaces R^n 0:47:41
  • [X] 6. Column Space and Nullspace 0:46:01
  • [X] 9. Independence, Basis, and Dimension 0:50:14
  • [X] 10. The Four Fundamental Subspaces 0:49:20
  • [X] 11. Matrix Spaces; Rank 1; Small World Graphs 0:45:55
  • [X] 14. Orthogonal Vectors and Subspaces 0:49:47
  • [X] 15. Projections onto Subspaces 0:48:51
  • [X] 16. Projection Matrices and Least Squares 0:48:05
  • [X] 17. Orthogonal Matrices and Gram-Schmidt 0:49:09
  • [X] 21. Eigenvalues and Eigenvectors 0:51:22
  • [X] 22. Diagonalization and Powers of A 0:51:50
  • [X] 24. Markov Matrices; Fourier Series 0:51:11
  • [X] 25. Symmetric Matrices and Positive Definiteness 0:43:52
  • [X] 27. Positive Definite Matrices and Minima 0:50:40
  • [X] 29. Singular Value Decomposition 0:40:28
  • [X] 30. Linear Transformations and Their Matrices 0:49:27
  • [X] 31. Change of Basis; Image Compression 0:50:13
  • [X] 33. Left and Right Inverses; Pseudoinverse 0:41:52

• [ ] Fast.ai: Deep Learning for Coder (2020)

  • [X] Lesson 1
  • [X] Lesson 2
  • [X] Lesson 3
  • [X] Lesson 4
  • [ ] Lesson 5
  • [X] Lesson 6
  • [ ] Lesson 7
  • [X] Lesson 8

• [X] Full Stack Deep Learning

  • [X] Setting Up Machine learning Projects
  • [X] Infrastructure And Tooling
  • [X] Data Management
  • [X] Machine Learning Teams
  • [X] Training And Debugging
  • [X] Testing And Deployment

• [ ] CNN For Visual Recognition — cs231n

  • [X] Lecture 1 | Introduction to Convolutional Neural Networks
  • [X] Lecture 2 | Image Classification
  • [X] lecture 3 | Loss Function and Optimization
  • [X] lecture 4 | Introduction to Neural Networks
  • [X] Lecture 5 | Convulutional Neural Network
  • [X] Lecture 6 | Training Neural Network I
  • [X] Lecture 7 | Training Neural Network II
  • [X] Lecture 8 | Deep learning Software
  • [X] Lecture 9 | CNN Architectures
  • [X] Lecture 10 | Recurrent Neural Networks
  • [X] Lecture 11 | Detection and Segmentation
  • [X] Lecture 12 | Visualizing and Understanding
  • [X] Lecture 13 | Generative Models
  • [ ] Lecture 14 | Deep Reinforcement Learning
  • [ ] Lecture 15 | Efficient Methods and Hardware for Deep Learning
  • [ ] Lecture 16 | Adversarial Examples and Adversarial Training

• [ ] Learn eXtreme Gradient Boosting - State-of-art ML Algorithm for Kaggle Contest till date.

  • [X] Part I | Introduction and Regression with XGBoost
  • [X] Part II | Classification with XGboost
  • [X] Part III | Mathematical Details of XGBoost
  • [X] Part IV | Cool Optimization with XGBoost

MLOps Fundamentals - Machine Learning in Production

Software Engineering

• [X] The Twelve Factors
• [X] Book "Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations", 2018 by Nicole Forsgren et.al
• [X] Book "The DevOps Handbook" by Gene Kim, et al. 2016
• [X] State of DevOps 2019
• [X] Clean Code concepts adapted for machine learning and data science.
• [X] School of SRE

MLOps Core

• [X] Machine Learning Operations: You Design It, You Train It, You Run It!
• [X] MLOps SIG Specification
• [X] ML in Production
• [X] Awesome production machine learning: State of MLOps Tools and Frameworks
• [X] Udemy “Deployment of ML Models”
• [X] Full Stack Deep Learning
• [X] Engineering best practices for Machine Learning
• [X] :rocket: Putting ML in Production
• [X] Stanford MLSys Seminar Series
• [X] IBM ML Operationalization Starter Kit
• [X] Productize ML. A self-study guide for Developers and Product Managers building Machine Learning products.
• [X] MLOps (Machine Learning Operations) Fundamentals on GCP
• [X] ML full Stack preparation
• [X] Machine Learing Engineering in Production | DeepLearning AI

MLOps: Model Deployment and Serving

• [X] AI Infrastructure for Everyone: DeterminedAI
• [X] Deploying R Models with MLflow and Docker
• [X] What Does it Mean to Deploy a Machine Learning Model?
• [X] Software Interfaces for Machine Learning Deployment
• [X] Batch Inference for Machine Learning Deployment
• [X] AWS Cost Optimization for ML Infrastructure - EC2 spend
• [X] CI/CD for Machine Learning & AI
• [X] Itaú Unibanco: How we built a CI/CD Pipeline for machine learning with online training in Kubeflow
• [X] 101 For Serving ML Models
• [X] Deploying Machine Learning models to production — Inference service architecture patterns
• [X] Serverless ML: Deploying Lightweight Models at Scale
• [X] ML Model Rollout To Production. Part 1 | Part 2
• [X] Deploying Python ML Models with Flask, Docker and Kubernetes
• [X] Deploying Python ML Models with Bodywork

MLOps: Testing, Monitoring and Maintenance

• [X] Building dashboards for operational visibility (AWS)
• [X] Monitoring Machine Learning Models in Production
• [X] Effective testing for machine learning systems
• [X] Unit Testing Data: What is it and how do you do it?
• [X] How to Test Machine Learning Code and Systems (Accompanying code)
• [X] Wu, T., Dong, Y., Dong, Z., Singa, A., Chen, X. and Zhang, Y., 2020. Testing Artificial Intelligence System Towards Safety and Robustness: State of the Art. IAENG International Journal of Computer Science, 47(3).
• [X] Multi-Armed Bandits and the Stitch Fix Experimentation Platform
• [X] A/B Testing Machine Learning Models
• [X] Data validation for machine learning. Polyzotis, N., Zinkevich, M., Roy, S., Breck, E. and Whang, S., 2019. Proceedings of Machine Learning and Systems
• [X] Testing machine learning based systems: a systematic mapping
• [X] Explainable Monitoring: Stop flying blind and monitor your AI
• [X] WhyLogs: Embrace Data Logging Across Your ML Systems
• [X] Evidently AI. Insights on doing machine learning in production. (Vendor blog.)
• [X] The definitive guide to comprehensively monitoring your AI
• [X] Introduction to Unit Testing for Machine Learning
• [X] Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance
• [X] Test-Driven Development in MLOps Part 1

MLOps: Infrastructure

• [X] MLOps Infrastructure Stack Canvas
• [X] Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps
• [X] AI Infrastructure Alliance. Building the canonical stack for AI/ML
• [X] Linux Foundation AI Foundation
• [X] ML Infrastructure Tools for Production | Part 1 — Production ML — The Final Stage of the Model Workflow | Part 2 — Model Deployment and Serving
• [X] The MLOps Stack Template (by valohai)

Introduction to Computer Science

• [X] CS 10 - The Beauty and Joy of Computing - Spring 2015 - Dan Garcia - UC Berkeley InfoCoBuild
• [X] 6.0001 - Introduction to Computer Science and Programming in Python - MIT OCW
• [ ] 6.001 - Structure and Interpretation of Computer Programs, MIT
• [X] CS 50 - Introduction to Computer Science, Harvard University (cs50.tv)
• [X] CS 61A - Structure and Interpretation of Computer Programs [Python], UC Berkeley
• [X] CPSC 110 - Systematic Program Design [Racket], University of British Columbia
• [X] CS50's Understanding Technology
• [X] CSE 142 Computer Programming I (Java Programming), Spring 2016 - University of Washington
• [X] CS 1301 Intro to computing - Gatech
• [ ] CS 106A - Programming Methodology, Stanford University (Lecture Videos)
• [ ] CS 106B - Programming Abstractions, Stanford University (Lecture Videos)
• [ ] CS 106X - Programming Abstractions in C++ (Lecture Videos)
• [X] CS 107 - Programming Paradigms, Stanford University
• [ ] CmSc 150 - Introduction to Programming with Arcade Games, Simpson College
• [ ] LINFO 1104 - Paradigms of computer programming, Peter Van Roy, Université catholique de Louvain, Belgium - EdX
• [ ] FP 101x - Introduction to Functional Programming, TU Delft
• [ ] Introduction to Problem Solving and Programming - IIT Kanpur
• [ ] Introduction to programming in C - IIT Kanpur
• [X] Programming in C++ - IIT Kharagpur
• [X] Python Boot Camp Fall 2016 - Berkeley Institute for Data Science (BIDS)
• [ ] CS 101 - Introduction to Computer Science - Udacity
• [X] 6.00SC - Introduction to Computer Science and Programming (Spring 2011) - MIT OCW
• [X] 6.00 - Introduction to Computer Science and Programming (Fall 2008) - MIT OCW
• [X] 6.01SC - Introduction to Electrical Engineering and Computer Science I - MIT OCW
• [X] Modern C++ Course (2018) - Bonn University
• [X] Modern C++ (Lecture & Tutorials, 2020, Vizzo & Stachniss) - University of Bonn

Software Engineering — CRUX

• Object Oriented Design
  • Object-oriented Program Design and Software Engineering - Aduni
  • OOSE - Object-Oriented Software Engineering, Dr. Tim Lethbridge
  • Object Oriented Systems Analysis and Design (Systems Analysis and Design in a Changing World)
  • CS 251 - Intermediate Software Design (C++ version) - Vanderbilt University
  • OOSE - Software Dev Using UML and Java
  • Object-Oriented Analysis and Design - IIT Kharagpur
  • CS3 - Design in Computing - Richard Buckland UNSW
  • Informatics 1 - Object-Oriented Programming 2014/15- University of Edinburgh
  • Software Engineering with Objects and Components 2015/16- University of Edinburgh
• Software Engineering
  • Computer Science 169- Software Engineering - Spring 2015 - UCBerkeley
  • CS 5150 - Software Engineering, Fall 2014 - Cornell University
  • Introduction to Service Design and Engineering - University of Trento, Italy
  • CS 164 Software Engineering - Harvard
  • System Analysis and Design - IISC Bangalore
  • Software Engineering - IIT Bombay
  • Dependable Systems (SS 2014)- HPI University of Potsdam
  • Software Testing - IIT Kharagpur
  • Informatics 2C - Software Engineering 2014/15- University of Edinburgh
• Software Architecture
  • CS 411 - Software Architecture Design - Bilkent University
  • MOOC - Software Architecture & Design - Udacity

Lets Understand Research Methodology

• [X] Efficient Estimation of Word Representations in Vector Space — Word2Vec
• [X] eXtreme Gradient Boosting — A Scalable Tree Boosting System
• [ ] Paper: A Neural Probabilistic Language Model
• [ ] Paper: Efficient Estimation of Word Representations in Vector Space
• [ ] Paper: Sequence to Sequence Learning with Neural Networks
• [ ] Paper: Neural Machine Translation by Jointly Learning to Align and Translate
• [ ] Paper: Attention Is All You Need
• [ ] Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• [ ] Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
• [ ] Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
• [ ] Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
• [ ] Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
• [ ] Paper: Collaborative Filtering for Implicit Feedback Datasets
• [ ] Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
• [ ] Paper: Factorization Machines
• [ ] Paper: Wide & Deep Learning for Recommender Systems
• [ ] Paper: Multiword Expressions: A Pain in the Neck for NLP
• [ ] Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
• [ ] Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
• [ ] Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
• [ ] Paper: A Simple Framework for Contrastive Learning of Visual Representations
• [ ] Paper: Self-Supervised Learning of Pretext-Invariant Representations
• [ ] Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• [ ] Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning
• [ ] Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
• [ ] Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
• [ ] Paper: Zero-shot Text Classification With Generative Language Models
• [ ] Paper: How to Fine-Tune BERT for Text Classification?
• [ ] Paper: Universal Sentence Encoder
• [ ] Paper: Enriching Word Vectors with Subword Information
• [ ] Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
• [ ] Paper: Temporal Ensembling for Semi-Supervised Learning
• [ ] Paper: Boosting Self-Supervised Learning via Knowledge Transfer
• [ ] Paper: Follow-up Question Generation
• [ ] Paper: The Hardware Lottery
• [ ] Paper: Question Generation via Overgenerating Transformations and Ranking
• [ ] Paper: Good Question! Statistical Ranking for Question Generation
• [ ] Paper: Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
• [ ] Paper: Neural Text Generation: A Practical Guide
• [ ] Paper: Pest Management In Cotton Farms: An AI-System Case Study from the Global South
• [ ] Paper: BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
• [ ] Paper: On the surprising similarities between supervised and self-supervised models
• [ ] Paper: All-but-the-Top: Simple and Effective Postprocessing for Word Representations
• [ ] Paper: Simple and Effective Dimensionality Reduction for Word Embeddings
• [ ] Paper: AutoCompete: A Framework for Machine Learning Competitions
• [ ] Paper: Cost-effective Deployment of BERT Models in Serverless Environment
• [ ] Paper: Evaluating Large Language Models Trained on Code
• [ ] Paper: What Does BERT Learn about the Structure of Language?
• [ ] Paper: What do RNN Language Models Learn about Filler–Gap Dependencies?
• [ ] Paper: Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world
• [ ] Paper: MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
• [ ] Paper: Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs
• [ ] Paper: Show and Tell: A Neural Image Caption Generator
• [ ] Paper: The Curious Case of Neural Text Degeneration
• [ ] Paper: Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
• [ ] Paper : Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery
• [ ] Fine Tuning Unet For Ultrasound Image Segmentation