- 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
- 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
- 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
- Radon Transformation
- Fourier Transform
- Hankel Transformation
- Cross Correlation - Generalized Projection of Function Into Reference Vector
- Autocorrelation
- Convolution
- Correlation
- Laplace Transformation
- Kullback–Leibler Divergence
- 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
- ML Ops: Machine Learning as an Engineering Discipline
- Rules of Machine Learning : Best Practices for ML Engineering
- Regular Expression — Official Python Regex Module
- Learn Regex
- Regex Made Easy With Real Python
- Regular Expressions Demystified
- 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
- 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!
- 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
- 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
- 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
- Version Control Via Git
- A Sucessful Git Branching Model
- Git & Github Crash Course
- Everything About Git & Gitbash
- 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
- Linear Algebra
- Singular Value Decomposition
- Basic Pattern Recognition
- Reduce The Dimesnion — PCA
- Guide To Kalman Filtering
- Fourtier Transforms
- 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
- 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
- StatQuest: Fitting a line to data, aka least squares, aka linear regression.
-
Machine Learning Engineering for Production (MLOps) Specialization — COURSERA SPECIALIZATION
-
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
- 1. The Geometry of Linear Equations
-
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.
- 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
- 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
- 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
- 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 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)
- 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
- 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
- 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