Awesome Open Source
Awesome Open Source

# learning

A running log of things I'm learning to build strong core software engineering skills while also expanding breadth of knowledge on adjacent technologies a little bit everyday.

## Core Skills

Python Programming
Data Structures and Algorithms
Concept Resource Done
Book: Grokking Algorithms
Udacity: Intro to Data Structures and Algorithms
Linux & Command Line
Concept Resource Done
Datacamp: Introduction to Shell for Data Science
Datacamp: Introduction to Bash Scripting
Datacamp: Data Processing in Shell
Lecture 1: Course Overview + The Shell (2020) `0:48:16`
Lecture 2: Shell Tools and Scripting (2020) `0:48:55`
Lecture 3: Editors (vim) (2020) `0:48:26`
Lecture 4: Data Wrangling (2020) `0:50:03`
Lecture 5: Command-line Environment (2020) `0:56:06`
Lecture 7: Debugging and Profiling (2020) `0:54:13`
Lecture 8: Metaprogramming (2020) `0:49:52`
Lecture 9: Security and Cryptography (2020) `1:00:59`
Udacity: Linux Command Line Basics
Udacity: Shell Workshop
Udacity: Configuring Linux Web Servers
Article: Streamline your projects using Makefile
Article: Understand Linux Load Averages and Monitor Performance of Linux
Calmcode: makefiles
Calmcode: entr
Version Control
Code Editor / IDE
Test-Driven Development
Web Technology
Backend & Web Servers
Databases
Production Environment
System and Infrastructure Design
Mathematics
Concept Resource Done
Probability Article: Entropy, Cross Entropy, and KL Divergence
Article: Interview Guide to Probability Distributions
Article: Entropy of a probability distribution — in layman’s terms
Article: KL Divergence — in layman’s terms
Article: Probability Distributions
Article: Cross-Entropy and KL Divergence
Article: Why Randomness Is Information?
Article: Basic Probability Theory
Datacamp: Foundations of Probability in Python
Statistics Datacamp: Introduction to Statistics
Datacamp: Introduction to Statistics in Python
Datacamp: Hypothesis Testing in Python
Datacamp: Statistical Thinking in Python (Part 1)
Datacamp: Statistical Thinking in Python (Part 2)
Datacamp: Statistical Simulation in Python
edX: Essential Statistics for Data Analysis using Excel
StatQuest: Histograms, Clearly Explained `0:03:42`
StatQuest: What is a statistical distribution? `0:05:14`
StatQuest: The Normal Distribution, Clearly Explained!!! `0:05:12`
Statistics Fundamentals: Population Parameters `0:14:31`
Statistics Fundamentals: The Mean, Variance and Standard Deviation `0:14:22`
StatQuest: What is a statistical model? `0:03:45`
StatQuest: Sampling A Distribution `0:03:48`
Hypothesis Testing and The Null Hypothesis `0:14:40`
Alternative Hypotheses: Main Ideas!!! `0:09:49`
p-values: What they are and how to interpret them `0:11:22`
How to calculate p-values `0:25:15`
p-hacking: What it is and how to avoid it! `0:13:44`
Statistical Power, Clearly Explained!!! `0:08:19`
Power Analysis, Clearly Explained!!! `0:16:44`
Covariance and Correlation Part 1: Covariance `0:22:23`
Covariance and Correlation Part 2: Pearson's Correlation `0:19:13`
StatQuest: R-squared explained `0:11:01`
The Central Limit Theorem `0:07:35`
StatQuickie: Standard Deviation vs Standard Error `0:02:52`
StatQuest: The standard error `0:11:43`
StatQuest: Technical and Biological Replicates `0:05:27`
StatQuest - Sample Size and Effective Sample Size, Clearly Explained `0:06:32`
Bar Charts Are Better than Pie Charts `0:01:45`
StatQuest: Boxplots, Clearly Explained `0:02:33`
StatQuest: Logs (logarithms), clearly explained `0:15:37`
StatQuest: Confidence Intervals `0:06:41`
StatQuickie: Thresholds for Significance `0:06:40`
StatQuickie: Which t test to use `0:05:10`
StatQuest: One or Two Tailed P-Values `0:07:05`
The Binomial Distribution and Test, Clearly Explained!!! `0:15:46`
StatQuest: Quantiles and Percentiles, Clearly Explained!!! `0:06:30`
StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained `0:06:55`
StatQuest: Quantile Normalization `0:04:51`
StatQuest: Probability vs Likelihood `0:05:01`
StatQuest: Maximum Likelihood, clearly explained!!! `0:06:12`
Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0 `0:09:39`
Why Dividing By N Underestimates the Variance `0:17:14`
Maximum Likelihood for the Binomial Distribution, Clearly Explained!!! `0:11:24`
Maximum Likelihood For the Normal Distribution, step-by-step! `0:19:50`
StatQuest: Odds and Log(Odds), Clearly Explained!!! `0:11:30`
StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! `0:16:20`
Live 2020-04-20!!! Expected Values `0:33:00`
Udacity: Statistics
Udacity: Intro to Inferential Statistics
Calculus The Essence of Calculus, Chapter 1 `0:17:04`
The paradox of the derivative | Essence of calculus, chapter 2 `0:17:57`
Derivative formulas through geometry | Essence of calculus, chapter 3 `0:18:43`
Visualizing the chain rule and product rule | Essence of calculus, chapter 4 `0:16:52`
What's so special about Euler's number e? | Essence of calculus, chapter 5 `0:13:50`
Implicit differentiation, what's going on here? | Essence of calculus, chapter 6 `0:15:33`
Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7 `0:18:26`
Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8 `0:20:46`
What does area have to do with slope? | Essence of calculus, chapter 9 `0:12:39`
Higher order derivatives | Essence of calculus, chapter 10 `0:05:38`
Taylor series | Essence of calculus, chapter 11 `0:22:19`
What they won't teach you in calculus `0:16:22`
But what is a Neural Network? | Deep learning, chapter 1 `0:19:13`
Gradient descent, how neural networks learn | Deep learning, chapter 2 `0:21:01`
What is backpropagation really doing? | Deep learning, chapter 3 `0:13:54`
Backpropagation calculus | Deep learning, chapter 4 `0:10:17`
Article: A Visual Tour of Backpropagation
Linear Algebra Vectors, what even are they? | Essence of linear algebra, chapter 1 `0:09:52`
Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2 `0:09:59`
Linear transformations and matrices | Essence of linear algebra, chapter 3 `0:10:58`
Matrix multiplication as composition | Essence of linear algebra, chapter 4 `0:10:03`
Three-dimensional linear transformations | Essence of linear algebra, chapter 5 `0:04:46`
The determinant | Essence of linear algebra, chapter 6 `0:10:03`
Inverse matrices, column space and null space | Essence of linear algebra, chapter 7 `0:12:08`
Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8 `0:04:27`
Dot products and duality | Essence of linear algebra, chapter 9 `0:14:11`
Cross products | Essence of linear algebra, Chapter 10 `0:08:53`
Cross products in the light of linear transformations | Essence of linear algebra chapter 11 `0:13:10`
Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12 `0:12:12`
Change of basis | Essence of linear algebra, chapter 13 `0:12:50`
Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14 `0:17:15`
Abstract vector spaces | Essence of linear algebra, chapter 15 `0:16:46`
Article: Introduction to Linear Algebra for Applied Machine Learning with Python
Article: Relearning Matrices as Linear Functions
Article: You Could Have Come Up With Eigenvectors - Here's How
Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Search
Article: Interactive Visualization of Why Eigenvectors Matter
Book: Basics of Linear Algebra for Machine Learning
Computational Linear Algebra for Coders
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`
Udacity: Eigenvectors and Eigenvalues
Udacity: Linear Algebra Refresher
Interview Preparation

## Specialized Skills

Machine Learning Fundamentals
Concept Resource Done
Regression Article: Linear regression
Article: Polynomial regression
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: Fiitting a curve to data, aka lowess, aka loess `0:10:10`
Naive Bayes Article: Naive Bayes classification
Naive Bayes, Clearly Explained!!! `0:15:12`
Gaussian Naive Bayes, Clearly Explained!!! `0:09:41`
Logistic Regression Article: Logistic regression
Datacamp: Foundations of Predictive Analytics in Python (Part 1)
Datacamp: Foundations of Predictive Analytics in Python (Part 2)
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`
Article: One-vs-Rest strategy for Multi-Class Classification
Article: Multi-class Classification — One-vs-All & One-vs-One
Article: One-vs-Rest and One-vs-One for Multi-Class Classification
Decision Trees Article: Decision trees
StatQuest: Decision Trees `0:17:22`
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data `0:05:16`
Decision Trees in Python from Start to Finish `1:06:23`
Regression Trees, Clearly Explained!!! `0:22:33`
How to Prune Regression Trees, Clearly Explained!!! `0:16:15`
KNN Article: K-nearest neighbors
SVM Article: Support Vector Machines
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`
Bagging Article: Random forests
StatQuest: Random Forests Part 1 - Building, Using and Evaluating `0:09:54`
StatQuest: Random Forests Part 2: Missing data and clustering `0:11:53`
Boosting Article: Boosted trees
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`
Datacamp: Ensemble Methods in Python
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`
Datacamp: Extreme Gradient Boosting with XGBoost
Dimensionality Reduction 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`
Clustering StatQuest: Hierarchical Clustering `0:11:19`
StatQuest: K-means clustering `0:08:57`
StatQuest: K-nearest neighbors, Clearly Explained `0:05:30`
Datacamp: Customer Segmentation in Python
Datacamp: Unsupervised Learning in Python
Udacity: Segmentation and Clustering
Neural Networks Coursera: Neural Networks and Deep Learning
Fast.ai: Deep Learning for Coder (2020)
Gradient Descent, Step-by-Step `0:23:54`
The Chain Rule `0:18:23`
Stochastic Gradient Descent, Clearly Explained!!! `0:10:53`
Article: Neural networks: activation functions
Article: Neural networks: training with backpropagation
Article: Neural Network from scratch-part 1
Article: Neural Network from scratch-part 2
Article: Perceptron to Deep-Neural-Network
Neural Networks from Scratch - P.1 Intro and Neuron Code `0:16:59`
Neural Networks from Scratch - P.2 Coding a Layer `0:15:06`
Neural Networks from Scratch - P.3 The Dot Product `0:25:17`
Neural Networks from Scratch - P.4 Batches, Layers, and Objects `0:33:46`
Neural Networks from Scratch - P.5 Hidden Layer Activation Functions `0:40:05`
An overview of gradient descent optimization algorithms
Article: Optimization for Deep Learning Highlights in 2017
Article: Setting the learning rate of your neural network
Article: Dismantling Neural Networks to Understand the Inner Workings with Math and Pytorch
Article: Connections: Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks
Article: MLE and MAP — in layman’s terms
Article: Cross-entropy for classification
Evaluation Metrics Article: Measuring Performance: AUPRC and Average Precision
Article: Measuring Performance: AUC (AUROC)
Article: Measuring Performance: The Confusion Matrix
Article: Measuring Performance: Accuracy
Article: ROC Curves: Intuition Through Visualization
Article: Precision, Recall, Accuracy, and F1 Score for Multi-Label Classification
Article: The Complete Guide to AUC and Average Precision: Simulations and Visualizations
Article: Proxy Metrics
Youtube: Applied ML 2020 - 09 - Model Evaluation and Metrics `1:18:23`
Article: Validating your Machine Learning Model
Youtube: Machine Learning Fundamentals: Cross Validation `0:06:04`
Youtube: Machine Learning Fundamentals: The Confusion Matrix `0:07:12`
Youtube: Machine Learning Fundamentals: Sensitivity and Specificity `0:11:46`
Youtube: Machine Learning Fundamentals: Bias and Variance `0:06:36`
Youtube: ROC and AUC, Clearly Explained! `0:16:26`
Article: The correct way to evaluate online machine learning models
Article: Best Use of Train/Val/Test Splits, with Tips for Medical Data
Machine Learning Libraries
Docker and Containerization
Cloud Computing
Natural Language Processing
Concept Resource Done
Fundamentals Stanford CS224U: Natural Language Understanding | Spring 2019
Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019
Natural Language Processing with Transformers Book
Preprocessing Article: Fixing common Unicode mistakes with Python â€“ after they’ve been made
Datacamp: Feature Engineering for NLP in Python
Datacamp: Natural Language Processing Fundamentals in Python
Datacamp: Regular Expressions in Python
Tokenization Article: 3 subword algorithms help to improve your NLP model performance
Keyword Extraction Article: Build A Keyword Extraction API with Spacy, Flask, and FuzzyWuzzy
Article: Unsupervised Auto-labeling of Websites
Article: Keyword Extraction with BERT
Article: Topic Modeling for Keyword Extraction
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings | Research Paper Walkthrough `0:21:23`
Embeddings Article: The Illustrated Word2vec
Article: Intuition & Use-Cases of Embeddings in NLP & beyond
Article: Learning Word Embedding
Article: On word embeddings - Part 1
Article: On word embeddings - Part 2: Approximating the Softmax
Article: On word embeddings - Part 3: The secret ingredients of word2vec
Article: Word embeddings in 2017: Trends and future direction
Rasa Algorithm Whiteboard - Embeddings 1: Just Letters `0:13:48`
Rasa Algorithm Whiteboard - Embeddings 2: CBOW and Skip Gram `0:19:24`
Rasa Algorithm Whiteboard - Embeddings 3: GloVe `0:19:12`
Rasa Algorithm Whiteboard - Embeddings 4: Whatlies `0:14:03`
Rasa Algorithm Whiteboard - StarSpace `0:11:46`
Rasa Algorithm Whiteboard - Countvectors `0:13:32`
Rasa Algorithm Whiteboard - Subword Embeddings `0:11:58`
Rasa Algorithm Whiteboard - Implementation of Subword Embeddings `0:10:01`
Rasa Algorithm Whiteboard - BytePair Embeddings `0:12:44`
Vector 1 Word Meaning `0:09:09`
Vector 2 Vector Semantics `0:06:37`
Vector 3 Words and Vectors `0:05:16`
Vector 4 Cosine Similarity `0:04:23`
Vector 5 TF IDF `0:05:32`
Vector 6 Word2vec `0:07:39`
Vector 7 Learning in Word2vec `0:07:36`
Vector 8 Properties of Embeddings `0:06:08`
Youtube: Applied ML 2020 - 17 - Word vectors and document embeddings `1:03:04`
RNN Article: Long Short-Term Memory: From Zero to Hero with PyTorch
Datacamp: RNN for Language Modeling
Article: Introduction to recurrent neural networks
Article: Explaining RNNs without neural networks
Article: Understanding LSTM Networks
Article: The Unreasonable Effectiveness of Recurrent Neural Networks
Article: Under the Hood of RNNs
Article: Exploring LSTMs
Article: Making sense of LSTMs by example
Article: Building RNNs is Fun with PyTorch and Google Colab
CMU Neural Nets for NLP 2021 (5): Recurrent Neural Networks `0:38:50`
CMU Advanced NLP 2021 (5): Recurrent Neural Networks `1:13:43`
Notebook: TextRNN - Predict Next Step
Notebook: TextLSTM - Autocomplete
Youtube: RNN and LSTM `26:13`
Article: Understanding building blocks of ULMFIT
Article: Character level language model RNN
Text CNN Article: Understanding Convolutional Neural Networks for NLP
Transformers Article: Attention? An Other Perspective!: Part 1
UMass CS685 (Advanced NLP): Implementing a Transformer `1:12:36`
Article: Attention and Memory in Deep Learning and NLP
Article: Attention? An Other Perspective!: Part 2
Article: Attention? An Other Perspective!: Part 3
Article: Attention? An Other Perspective!: Part 4
Article: Attention? An Other Perspective!: Part 5
Rasa Algorithm Whiteboard - Attention 1: Self Attention `0:14:32`
Rasa Algorithm Whiteboard - Attention 2: Keys, Values, Queries `0:12:26`
Rasa Algorithm Whiteboard - Attention 3: Multi Head Attention `0:10:55`
Rasa Algorithm Whiteboard: Attention 4 - Transformers `0:14:34`
Youtube: A brief history of the Transformer architecture in NLP
Youtube: The Transformer neural network architecture explained. “Attention is all you need” (NLP)
Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision
Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)
Article: The Illustrated Transformer
Article: The Annotated Transformer
Article: A Deep Dive into the Reformer
Article: A Survey of Long-Term Context in Transformers
Article: Why Rasa uses Sparse Layers in Transformers
Article: Transformer-based Encoder-Decoder Models
Article: Understanding BigBird's Block Sparse Attention"
Article: The Transformer Family
Article: Hugging Face Reads - 01/2021 - Sparsity and Pruning
Article: Hugging Face Reads, Feb. 2021 - Long-range Transformers
Article: The Transformer Explained
LSBert: A Simple Framework for Lexical Simplification | Research Paper Walkthrough `0:20:27`
SpanBERT: Improving Pre-training by Representing and Predicting Spans | Research Paper Walkthrough `0:14:21`
T5: Exploring Limits of Transfer Learning with Text-to-Text Transformer (Research Paper Walkthrough) `0:12:47`
Hierarchical Transformers for Long Document Classification (Research Paper Walkthrough) `0:12:46`
UMass CS685 (Advanced NLP): Attention mechanisms `0:48:53`
UMass CS685 (Advanced NLP): Better BERTs `0:52:23`
UMass CS685 (Advanced NLP): Retrieval-augmented language models `0:52:13`
UMass CS685 (Advanced NLP): Model distillation and security threats `1:09:25`
UMass CS685 (Advanced NLP): vision + language `1:06:28`
UMass CS685 (Advanced NLP): Intermediate fine-tuning `1:10:35`
UMass CS685 (Advanced NLP): probe tasks `0:54:30`
UMass CS685 (Advanced NLP): semantic parsing `0:48:49`
UMass CS685 (Advanced NLP): commonsense reasoning (guest lecture by Lorraine Li) `0:58:53`
BERT Article: Deconstructing BERT
Article: Maximizing BERT model performance
Article: The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
Article: Smart Batching Tutorial - Speed Up BERT Training
Article: A review of BERT based models
Article: Understanding BERT’s Semantic Interpretations
Article: Examining BERT’s raw embeddings
Semantic Search Article: Semantic Search On Documents
7 1 Introduction to Information Retrieval 9 16 `0:09:16`
7 2 Term Document Incidence Matrices 8 59 `0:08:59`
7 3 The Inverted Index 10 42 `0:10:43`
7 4 Query Processing with the Inverted Index 6 43 `0:06:44`
7 5 The Boolean Retrieval Model 14 06 `0:14:07`
7 6 Phrase Queries and Positional Indexes 19 45 `0:19:46`
8 1 Introducing Ranked Retrieval 4 27 `0:04:27`
8 2 Scoring with the Jaccard Coefficient 5 06 `0:05:07`
8 3 Term Frequency Weighting 5 59 `0:06:00`
8 4 Inverse Document Frequency Weighting 10 16 `0:10:17`
8 5 TF IDF Weighting 3 42 `0:03:42`
8 6 The Vector Space Model 16 22 `0:16:23`
8 7 Calculating TF IDF Cosine Scores 12 47 `0:12:48`
8 8 Evaluating Search Engines 9 02 `0:09:03`
Article: Locality-sensitive Hashing and Singular to Plural Noun Conversion
Article: Haystack: The State of Search in 2021
Article: Search (Pt 1) — A Gentle Introduction
Article: Search (Pt 2) — A Semantic Horse Race
Article: Search (Pt 3) — Elastic Transformers
Article: Semantic search using BERT embeddings
Article: What Semantic Search Can do for You
Article: How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning
Article: Building a sentence embedding index with fastText and BM25
Article: String Matching with BERT, TF-IDF, and more!
Article: Document search with fragment embeddings
Article: Text Similarities : Estimate the degree of similarity between two texts
Article: How we used Universal Sentence Encoder and FAISS to make our search 10x smarter
Article: Using embeddings to help find similar restaurants in Search
Article: Evolution of and experiments with feed ranking at Swiggy
Article: Personalizing Swiggy POP Recommendations
Article: Fan(s)tastic: Search for blazing-fast results
Article: Find My Food: Semantic Embeddings for Food Search Using Siamese Networks
Article: Learning To Rank Restaurants
Article: Comparison Of Ngram Fuzzy Matching Approaches
Article: String similarity — the basic know your algorithms guide!
Youtube: Billion-scale Approximate Nearest Neighbor Search
Youtube: Data Science - Fuzzy Record Matching
Youtube: Minimum Edit Distance Dynamic Programming
Youtube: Cheuk Ting Ho - Fuzzy Matching Smart Way of Finding Similar Names Using Fuzzywuzzy
Youtube: What's in a Name? Fast Fuzzy String Matching - Seth Verrinder & Kyle Putnam - Midwest.io 2015
Youtube: Jiaqi Liu Fuzzy Search Algorithms How and When to Use Them PyCon 2017
Youtube: 1 + 1 = 1 or Record Deduplication with Python | Flávio Juvenal @ PyBay2018
Youtube: Mike Mull: The Art and Science of Data Matching
Youtube: Approximate nearest neighbors and vector models, introduction to Annoy
Advanced Information Retrieval 2021 - 2021 Course Introduction `0:21:39`
Advanced Information Retrieval 2021: Crash Course IR - Fundamentals `0:46:31`
Advanced Information Retrieval 2021: Crash Course IR - Evaluation `0:37:15`
Advanced Information Retrieval 2021: Crash Course IR - Test Collections `0:51:12`
Advanced Information Retrieval 2021: Word Representation Learning `0:42:02`
Advanced Information Retrieval 2021: Sequence Modelling with CNNs and RNNs `0:55:04`
Advanced Information Retrieval 2021: Transformer and BERT Pre-training `0:47:15`
Advanced Information Retrieval 2021: Introduction to Neural Re-Ranking `0:59:20`
Advanced Information Retrieval 2021: Transformer Contextualized Re-Ranking `0:49:06`
Advanced Information Retrieval 2021: Domain Specific Applications `0:38:32`
Advanced Information Retrieval 2021: Dense Retrieval ❤ Knowledge Distillation `0:59:28`
Introduction to Dense Text Representations - Part 1 `0:12:56`
Introduction to Dense Text Representations - Part 2 `0:23:13`
Introduction to Dense Text Representation - Part 3 `0:38:07`
Training State-of-the-Art Sentence Embedding Models `0:43:43`
NER Article: Unsupervised NER using BERT
Introduction to Named Entity Tagging `0:05:06`
Introduction to Part of Speech Tagging `0:09:03`
Article: Zero shot NER using RoBERTA
Article: Existing Tools for Named Entity Recognition
Article: Solving NER with BERT for any entity type with very little training data (compared to past approaches)
Article: What is Hidden in the Hidden Markov Model?
Article: Part of Speech Tagging with Hidden Markov Chain Models
Article: Named-Entity evaluation metrics based on entity-level
Summarization Article: Automatically Summarize Trump’s State of the Union Address
Leveraging BERT for Extractive Text Summarization on Lectures | Research Paper Walkthrough `0:20:10`
A Supervised Approach to Extractive Summarisation of Scientific Papers | Research Paper Walkthrough `0:19:01`
Text Summarization of COVID-19 Medical Articles using BERT and GPT-2 | Research Paper Walkthrough `0:21:52`
Extractive & Abstractive Summarization with Transformer Language Models | Research Paper Walkthrough `0:16:58`
Unsupervised Multi-Document Summarization using Neural Document Model | Research Paper Walkthrough `0:15:11`
SummPip: Multi-Document Summarization with Sentence Graph Compression | Research Paper Walkthrough `0:16:54`
PEGASUS: Pre-training with Gap-Sentences for Abstractive Summarization | Research Paper Walkthrough `0:15:04`
On Generating Extended Summaries of Long Documents (Research Paper Walkthrough) `0:14:24`
Multilingual NLP Article: How to Apply BERT to Arabic and Other Languages
Article: A survey of cross-lingual word embedding models
Article: Unsupervised Cross-lingual Representation Learning
Article: DaCy: New Fast and Efficient State-of-the-Art in Danish NLP!
Article: Why You Should Do NLP Beyond English
CMU: Low-resource NLP Bootcamp 2020
CMU Multilingual NLP 2020
Domain Adaptation Article: Domain-Specific BERT Models
Text Generation Article: Text Generation
UMass CS685 (Advanced NLP): Text generation decoding and evaluation `1:02:32`
UMass CS685 (Advanced NLP): Paraphrase generation `1:10:59`
Article: The Illustrated GPT-2 (Visualizing Transformer Language Models)
Article: Controlling Text Generation with Plug and Play Language Models
Article: Poor man’s GPT-3: Few shot text generation with T5 Transformer
Article: Reducing Toxicity in Language Models
Article: How to Implement a Beam Search Decoder for Natural Language Processing
Article: Perplexity Intuition (and its derivation)
Article: The Annotated GPT-2
Datacamp: Natural Language Generation in Python
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Zero-Shot NLP Article: Zero-Shot Learning in Modern NLP
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Youtube: Deep Learning (for Audio) with Python
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