kburgess23 / keep-learning

Resources and blogs, curated for learning, computer science, mathematics, data science, computer vision, natural language processing, and machine learning. Pull requests and feedbacks are heartly welcomed

Keep Learning

Daily Motivation

Table of Contents

Philosophy - A Love of the Wisdom	Dive Into Research
Software Engineering - ML	MLOps Core
MLOps: Infrastructure	Blog Resources For Machine Learning
MLOps: Model Deployment and Serving	MLOps: Testing, Monitoring and Maintenance
Blogs — Be Better Everyday	MIT 6S191 Introduction to Deep Learning
Master The Computer Vision — List of blogs and tutorials for diving deep into CV	Software Engineering — CRUX
Introduction to Computer Science	Dive Into Deep Learning
Convolutional Neural Networks - CS231n - Stanford University	First Principles Of Computer Vision

Learn Philosophy

• The Development Of Neural Networks
• Receptive Field in CNN
• Standard Gaussian Distribution - Modelling Nature
• Convolution - Math Driving The Computer Vision
• Half Order Derivatives
• Fourier Transforms For Image Processing
• 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 Aepper Noise
• A noise robust convolutional neural network for image classification
• Xception: Deep Learning with Depthwise Separable Convolutions

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

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

Blogs — Be Better Everyday

Base of Modern Machine Learning

The Essense of Linear Algebra - 3 Blue 1 Brown

• 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

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

Machine Learning In Production

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

Regular Expressions — Irksome, Yet Useful

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

Numpy — Numerical Python

• Numpy Basics
• Numpy Fundamentals

Core Python

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

Be Pythonic

• Writing Pythonic Code — Transforming from messy code to beautiful pythonic code
• Write More Pythonic Code
• PEP 8 -- Style Guide for Python Code
• 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

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

Pandas — Be Able to Manipulate Data

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

Hands On Tensorflow and Keras

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

Control Your Code — Versioning

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

Developer Tools For ML

• Weight And Biases

Dive Into Deep learning

• Introduction
• Preliminaries

Convolutional Neural Networks

• Lecture 1 | Introduction to Convolutional Neural Networks
• Lecture 2 | Image Classification
• Lecture 3 | Loss Functions and Optimizations
• Lecture 4 | Introduction to Neural Networks
• Lecture 5 | Convolutional Neural Networks
• Lecture 6 | Training Neural Networks I
• Lecture 7 | Training Neural Networks II
• Lecture 8 | Deep Learning Software
• Lecture 9 | CNN Architectures
• Lecture 10 | Recurrent Neural Networks
• Lecture 11 | Detection and Segmentation
• Lecture 12 | Visualizing and Understanding
• 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

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

Beginner Level — Mathematics

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

Beginner Level — Image Procesing

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

Advanced Level

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

First Principles Of Computer Vision

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

Theory Of Classical Machine Learning

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

Video Tutorials — Deep Learning

• MIT 6S191 | Deep Learning Lectures

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

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

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

• MIT: 18.06 Linear Algebra (Professor Strang)

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

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

  • Lesson 1
  • Lesson 2
  • Lesson 3
  • Lesson 4
  • Lesson 5
  • Lesson 6
  • Lesson 7
  • Lesson 8

• Full Stack Deep Learning

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

• CNN For Visual Recognition — cs231n

  • Lecture 1 | Introduction to Convolutional Neural Networks
  • Lecture 2 | Image Classification
  • lecture 3 | Loss Function and Optimization
  • lecture 4 | Introduction to Neural Networks
  • Lecture 5 | Convulutional Neural Network
  • Lecture 6 | Training Neural Network I
  • Lecture 7 | Training Neural Network II
  • Lecture 8 | Deep learning Software
  • Lecture 9 | CNN Architectures
  • Lecture 10 | Recurrent Neural Networks
  • Lecture 11 | Detection and Segmentation
  • Lecture 12 | Visualizing and Understanding
  • 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.

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

MLOps Fundamentals - Machine Learning in Production

Software Engineering

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

MLOps Core

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

MLOps: Model Deployment and Serving

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

MLOps: Testing, Monitoring and Maintenance

• Building dashboards for operational visibility (AWS)
• Monitoring Machine Learning Models in Production
• Effective testing for machine learning systems
• Unit Testing Data: What is it and how do you do it?
• How to Test Machine Learning Code and Systems (Accompanying code)
• 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).
• Multi-Armed Bandits and the Stitch Fix Experimentation Platform
• A/B Testing Machine Learning Models
• Data validation for machine learning. Polyzotis, N., Zinkevich, M., Roy, S., Breck, E. and Whang, S., 2019. Proceedings of Machine Learning and Systems
• Testing machine learning based systems: a systematic mapping
• Explainable Monitoring: Stop flying blind and monitor your AI
• WhyLogs: Embrace Data Logging Across Your ML Systems
• Evidently AI. Insights on doing machine learning in production. (Vendor blog.)
• The definitive guide to comprehensively monitoring your AI
• Introduction to Unit Testing for Machine Learning
• Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance
• Test-Driven Development in MLOps Part 1

MLOps: Infrastructure

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

Introduction to Computer Science

• CS 10 - The Beauty and Joy of Computing - Spring 2015 - Dan Garcia - UC Berkeley InfoCoBuild
• 6.0001 - Introduction to Computer Science and Programming in Python - MIT OCW
• 6.001 - Structure and Interpretation of Computer Programs, MIT
• CS 50 - Introduction to Computer Science, Harvard University (cs50.tv)
• CS 61A - Structure and Interpretation of Computer Programs [Python], UC Berkeley
• CPSC 110 - Systematic Program Design [Racket], University of British Columbia
• CS50's Understanding Technology
• CSE 142 Computer Programming I (Java Programming), Spring 2016 - University of Washington
• 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)
• 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
• Programming in C++ - IIT Kharagpur
• Python Boot Camp Fall 2016 - Berkeley Institute for Data Science (BIDS)
• CS 101 - Introduction to Computer Science - Udacity
• 6.00SC - Introduction to Computer Science and Programming (Spring 2011) - MIT OCW
• 6.00 - Introduction to Computer Science and Programming (Fall 2008) - MIT OCW
• 6.01SC - Introduction to Electrical Engineering and Computer Science I - MIT OCW
• Modern C++ Course (2018) - Bonn University
• 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

• Efficient Estimation of Word Representations in Vector Space — Word2Vec
• 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