Natural Language Processing (NLP)

Venture into the fascinating world of NLP, a cutting-edge field of AI that empowers machines to comprehend and interact with human language. With NLP, unlock a myriad of transformative applications, including chatbots, language translation, sentiment analysis for social media, and voice assistants like Siri & Alexa.

Courses

• Natural Language Processing Specialization (opens in a new tab) by DeepLearning AI, include sentiment analysis, machine translation, text summarization, chatbot development, logistic regression, word vectors, deep learning models (RNNs, LSTMs, GRUs), and more. This specialization comprises four courses: Classification & Vector Spaces, Probabilistic Models, Sequence Models and Attention Models.

• Hugging Face NLP course (opens in a new tab): This course covers NLP with a focus on libraries within the Hugging Face ecosystem, including Transformers, Datasets, Tokenizers, Accelerate, and the Hugging Face Hub.

• Stanford CS224N: NLP with Deep Learning (opens in a new tab) offers a comprehensive course on NLP with Deep Learning. It introduces state-of-the-art research in Deep Learning for NLP, emphasizing the implementation, training, debugging, and expansion of neural network models for diverse language comprehension tasks.

• Stanford XCS224U: Natural Language Understanding (opens in a new tab): Instructed by Professor Christopher Potts, this Stanford Online course combines linguistic theory, NLP, and machine learning. Covered topics include supervised sentiment domain adaptation, retrieval augmented in-context learning, advanced behavioral evolution, and analysis methods in NLP.

• LLM University (opens in a new tab) by Cohere, The comprehensive NLP curriculum offers individuals a robust foundation, imparting skills to develop applications in semantic search, generation, classification, embeddings, and various NLP techniques. It covers basics to advanced topics in LLMs, empowering learners to maximize their potential. Practical, hands-on exercises allow individuals to build and deploy their models effectively.

Articles

• A Beginner’s Guide to Tokens, Vectors, and Embeddings in NLP (opens in a new tab) by Sascha Metzger, offers a beginner's guide to NLP essentials. It covers tokens, vectors, and embeddings, fundamental concepts used for text representation and analysis.

• AI Content Generation (opens in a new tab): This four-part series by Jon Stokes, delivers an extensive overview of content generation, exploring concepts, tools, diverse AI content tasks, their combinations for complex goals, and a deep dive into stable diffusion's core principles and workings. The series includes parts on: Machine Learning Basics, Tasks And Models, Deep dive into Stable Diffusion and What’s next.

Explainers

• The Attention Mechanism in Large Language Models (opens in a new tab): Serrano.Academy offers a three-part series exploring attention mechanisms, vital in large language models like GPT-3. It clarifies how attention enables selective input focus during output sequence generation, delves into mathematical concepts supporting DL models, and elucidates transformer models and their functioning. The series other parts: The math behind Attention: Keys, Queries, and Values matrices and What are Transformer Models and how do they work?

• A Complete Overview of Word Embeddings (opens in a new tab) by Assembly AI, gain insights into the significance of embeddings, their creation process, and their practical applications.

• What is Retrieval-Augmented Generation (RAG)? (opens in a new tab): Explains the framework of RAG and how it can help large language models be more accurate and up-to-date, also provides an anecdote to illustrate how large language models can have undesirable behavior and how RAG can improve factuality and reasoning abilities in NLP tasks.

Reference

• Hugging Face tasks (opens in a new tab) is the home for all Machine Learning tasks. Here you can find what you need to get started with a task: demos, use cases, models, datasets, and more.

• OpenAI Embeddings (opens in a new tab): OpenAI's embeddings are text-based vector representations that gauge text string relatedness. They're widely used in natural language and code tasks, including semantic search, text classification, and question answering.

Papers

• Efficient Estimation of Word Representations in Vector Space (opens in a new tab) (2013): Introduced two model architectures for word vector representations derived from large datasets, outperforming existing methods in word similarity tasks with enhanced accuracy and lower computational demands. These vectors excel in measuring syntactic and semantic word similarities.

• GloVe: Global Vectors for Word Representation (opens in a new tab) (2014): Proposed GloVe, a model that learns word meanings from co-occurrence statistics. It uses a global co-occurrence matrix to derive word vectors, demonstrating superior performance in various word analogy and similarity tasks compared to other methods.

• Deep contextualized word representations (opens in a new tab) (2018): Introduces novel word representations capturing both meaning and context in sentences. These representations stem from a deep bidirectional language model trained on extensive text. They excel in various NLP tasks, such as sentiment analysis and named entity recognition, surpassing other techniques in performance.

• Sequence to Sequence Learning with Neural Networks (opens in a new tab) (2014): Introduces the sequence-to-sequence model, a neural network for tasks like machine translation and text summarization. Comprising an encoder and a decoder, it effectively processes input sequences and generates output sequences, outperforming other methods in a range of NLP tasks.

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (opens in a new tab) (2020): Introduces a versatile fine-tuning method for retrieval-augmented generation (RAG) models, combining parametric and non-parametric memory for language generation. They employ a pre-trained neural retriever to fetch Wikipedia passages for input, achieving state-of-the-art results on knowledge-intensive NLP tasks. RAG models offer more precise, diverse, and factual language generation. (blog)

• Improving Language Understanding by Generative Pre-Training (opens in a new tab) (2018): Introduced Generative Pre-trained Transformer (GPT), enhancing natural language understanding through unsupervised learning, addressing the scarcity of labeled data. It introduces "generative pre-training," training a large neural network on vast unlabeled text corpora, followed by fine-tuning on specific tasks with labeled data. The approach combines transformers and unsupervised pre-training, showing improved language model performance and task-related advancements. (blog) (code)

• Language Models are Unsupervised Multitask Learners (opens in a new tab) (2019): Introduced GPT-2, enhancing natural language processing through unsupervised learning, showcasing a language model's ability to learn tasks without explicit supervision. Training on the WebText dataset, the model infers and performs tasks in natural language sequences, demonstrating superior performance in a zero-shot setting across various tasks compared to discriminatively trained models. (blog) (code)

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (opens in a new tab) (2018): Presents BERT, a pre-trained deep bidirectional transformer model. Trained on extensive text data with a masked language modeling objective, it excels in natural NLP like question answering and sentiment analysis, surpassing alternative methods in performance and versatility.

• ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (opens in a new tab) (2020): Introducing a more sample-efficient pre-training task, "replaced token detection," outperforming traditional masked language modeling like BERT. Instead of masking, it corrupts input by replacing tokens with alternatives from a small generator network. Demonstrates efficiency over Masked language modeling (MLM) by defining the task for all input tokens, yielding superior contextual representations.

• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (opens in a new tab) (2019): This study investigates transfer learning through a unified text-to-text transformer model, scrutinizing diverse architectural variants. It underscores the significance of transfer learning in NLP, outlining the experimental setup and benchmarks. The exploration includes the effects of pre-training objectives, model size, and data size, yielding state-of-the-art results.

• PaLM 2 (opens in a new tab): A superior language model by Google, excels in advanced reasoning, translation, and code generation. The next-gen PaLM is smaller yet more efficient, featuring enhanced performance with faster inference and reduced serving costs. Its diverse multilingual pre-training includes human and programming languages, equations, scientific papers, and web content. With improved architecture and varied task training, PaLM 2 caters to text generation, language translation, creative content creation, and informative question answering. (blog)