| 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 – 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 |
|
|
Youtube: BERT Research Series |
✓ |
|
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: Record linkage: Join for real life by Rhydwyn Mcguire |
✓ |
|
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 |
✓ |
|
BLEURT: Learning Robust Metrics for Text Generation | Research Paper Walkthrough 0:13:38 |
✓ |
|
Evaluation of Text Generation: A Survey | Human-Centric Evaluations | Research Paper Walkthrough 0:15:54 |
✓ |
|
Nucleus Sampling: The Curious Case of Neural Text Degeneration (Research Paper Walkthrough) 0:12:48 |
✓ |
| Spelling Correction |
Article: Rebuilding the most popular spellchecker. Part 1 |
✓ |
|
Article: Rebuilding the spellchecker, pt.2: Just look in the dictionary, they said! |
✓ |
|
Article: Rebuilding the spellchecker, pt.3: Lookup—compounds and solutions |
✓ |
|
Article: Rebuilding the spellchecker, pt.4: Introduction to suggest algorithm |
|
|
Article: Rebuilding the spellchecker: Hunspell and the order of edits |
|
|
Article: How to Use n-gram Models to Detect Format Errors in Datasets |
|
|
Article: Spelling Correction: How to make an accurate and fast corrector |
✓ |
|
Article: Speller100: Zero-shot spelling correction at scale for 100-plus languages |
✓ |
|
Article: Breaking the spell of the spelling check |
✓ |
|
Article: How to Write a Spelling Corrector |
✓ |
|
Article: Spellchecking by computer |
✓ |
|
Article: A Spellchecker Used to Be a Major Feat of Software Engineering |
✓ |
|
Article: 1000x Faster Spelling Correction algorithm (2012) |
✓ |
|
Youtube: How to build a custom spell checker using python NLP |
✓ |
| Topic Modeling |
Article: Automatic Topic Labeling in 2018: History and Trends |
|
|
Youtube: Applied ML 2020 - 16 - Topic models for text data 1:18:34 |
✓ |
|
Youtube: Extracting topics from reviews using NLP - Dr. Tal Perri |
✓ |
|
Article: Interactive Topic Modeling with BERTopic |
|
|
Article: Understanding Climate Change Domains through Topic Modeling |
|
|
Article: When Topic Modeling is Part of the Text Pre-processing |
|
|
Article: pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know |
|
|
Article: Topic Modeling with BERT |
✓ |
| Zero-Shot NLP |
Article: Zero-Shot Learning in Modern NLP |
✓ |
|
Article: Improved Few-Shot Text classification |
|
|
Article: Text classification from few training examples |
|
|
Article: Pattern-Exploiting Training |
|
| Paraphrasing |
Article: Paraphrasing |
|
| Conversational AI |
Article: What makes a good conversation? |
✓ |
|
Datacamp: Building Chatbots in Python |
✓ |
|
Rasa Algorithm Whiteboard - Diet Architecture 1: How it Works 0:23:27 |
✓ |
|
Rasa Algorithm Whiteboard - Diet Architecture 2: Design Decisions 0:15:06 |
✓ |
|
Rasa Algorithm Whiteboard - Diet Architecture 3: Benchmarking 0:22:34 |
✓ |
|
Rasa Algorithm Whiteboard - TED Policy 0:16:10 |
✓ |
|
Rasa Algorithm Whiteboard - TED in Practice 0:14:54 |
✓ |
|
Rasa Algorithm Whiteboard - Response Selection 0:12:07 |
✓ |
|
Rasa Algorithm Whiteboard - Response Selection: Implementation 0:09:25 |
✓ |
|
Dialog 1 Overview 0:03:11 |
|
|
Dialogue 2 Human Conversation 0:10:31 |
|
|
Dialogue 3 ELIZA 0:09:27 |
|
|
Dialogue 4 Corpus Chatbots 0:09:35 |
|
|
Dialogue 5 Frame Based Dialogue 0:07:41 |
|
|
Dialogue 6 Dialogue State Architecture 0:08:58 |
|
|
Dialogue 7 Dialogue State Architecture Policy and Generation 0:08:23 |
|
|
Dialogue 8 Evaluation 0:04:38 |
|
|
Dialogue 9 Design and Ethical Issues 0:03:29 |
|
|
YouTube: Level 3 AI Assistant Conference 2020 |
✓ |
|
Youtube: Conversational AI with Transformers and Rule-Based Systems 1:53:24 |
✓ |
|
TOD-BERT: Pre-trained Transformers for Task-Oriented Dialogue Systems (Research Paper Walkthrough) 0:15:25 |
✓ |
|
DialoGPT: Generative Training for Conversational Response Generation (Research Paper Walkthrough) 0:13:17 |
✓ |
|
Youtube: Transformers 🤗 to Rule Them All? Under the Hood of the AI Recruiter Chatbot 🤖, with Keisuke Inoue |
✓ |
|
Youtube: Chatbots Revisted | by Abhishek Thakur | Kaggle Days Warsaw |
✓ |
| Sentiment Analysis |
Article: NLP: Pre-trained Sentiment Analysis |
✓ |
|
Article: Key topics extraction and contextual sentiment of users reviews |
✓ |
|
Article: Aspect-Based Opinion Mining (NLP with Python) |
|
|
Datacamp: Sentiment Analysis in Python |
✓ |
|
Youtube: Sentiment Analysis: Key Milestones, Challenges and New Directions |
✓ |
|
Talk: EmoTag1200: Understanding the Association between Emojis and Emotions |
✓ |
|
Youtube: Real life aspects of opinion sentiment analysis within customer reviews - Dr. Jonathan Yaniv |
✓ |
|
Youtube: Deep Learning Methods for Emotion Detection from Text - Dr. Liron Allerhand |
✓ |
| Text Classification |
Article: Multi-Label Text Classification |
✓ |
| Text Clustering |
Article: Document clustering |
✓ |
|
Datacamp: Clustering Methods with SciPy |
✓ |
|
Article: Gaussian Mixture Models for Clustering |
|
| Explainability |
Article: Explain NLP models with LIME & SHAP |
|
|
Youtube: Explainability for Natural Language Processing |
|
| Usecases |
Article: How to solve 90% of NLP problems: a step-by-step guide |
✓ |
|
Article: Using an NLP Q&A System To Study Climate Hazards and Nature-Based Solutions |
✓ |
|
Article: How To Do Things With Words. And Counters |
|
|
Talk: Practical NLP for the Real World |
✓ |
|
Youtube: Design Considerations for building ML-Powered Search Applications - Mark Moyou |
✓ |
|
Youtube: Analyze Customer Feedback in Minutes, Not Months |
✓ |
|
Youtube: NLP in Feedback Analysis - Yue Ning |
✓ |
|
Youtube: Productionizing an unsupervised machine learning model to understand customer feedback |
✓ |
|
Youtube: Bringing innovation to online retail: automating customer service with NLP |
✓ |
|
Youtube: Transform customer service with machine learning (Google Cloud Next '17) |
✓ |
|
Youtube: Artificial Intelligence and Natural Language Processing in E-Commerce by Katherine Munro | smec |
✓ |
|
Youtube: The giant leaps in language technology -- and who's left behind | Kalika Bali |
✓ |
| Machine Translation |
Article: Introducing Translatotron: An End-to-End Speech-to-Speech Translation Model |
✓ |
|
Datacamp: Machine Translation in Python |
✓ |
| Libraries |
Datacamp: Advanced NLP with spaCy |
✓ |
|
Spacy Tutorial |
✓ |
|
Youtube: spaCy v3.0: Bringing State-of-the-art NLP from Prototype to Production 00:22:40 |
✓ |
|
Youtube: SpaCy for Digital Humanities with Python Tutorials |
✓ |
|
TextBlob Tutorial Series |
✓ |
|
YouTube: Intro to NLP with Spacy |
✓ |
| Huggingface |
How-to Use HuggingFace's Datasets - Transformers From Scratch #1 0:14:21 |
|
|
Build a Custom Transformer Tokenizer - Transformers From Scratch #2 0:14:17 |
|
|
Building MLM Training Input Pipeline - Transformers From Scratch #3 0:23:11 |
|
|
Training and Testing an Italian BERT - Transformers From Scratch #4 0:30:38 |
|
| Audio |
Datacamp: Spoken Language Processing in Python |
|
|
Youtube: Librosa Audio and Music Signal Analysis in Python | SciPy 2015 | Brian McFee |
✓ |
|
Youtube: Deep Learning (for Audio) with Python |
✓ |
| Gibberish Detection |
Youtube: Gibberish Detector |
✓ |
| Constituency Parsing |
Youtube: NLP Lecture 7 Constituency Parsing |
✓ |
|
Youtube: LING 83 Teaching Video: Constituency Parsing |
✓ |
| Question Answering |
UMass CS685 (Advanced NLP): Question answering 0:59:50 |
|
| Data Annotation |
UMass CS685 (Advanced NLP): Crowdsourced text data collection 0:58:31 |
|
| Ethics |
UMass CS685 (Advanced NLP): ethics in NLP 0:56:57 |
|
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
