Deep Learning with Keras - Python
Explore deep learning concepts and implement models using Keras in Python. Learn about neural networks, CNNs, RNNs, LSTM, and more for practical applications like chatbots.
Natural Language Processing
This course provides an understanding of natural language processing, its tools, techniques, philosophy and principle. It will cover Tokenization, Stop Words and Word Embedding.
Successes and Challenges in Neural Models for Speech and Language - Michael Collins
Explore neural models' impact on speech and language processing, examining successes, challenges, and key architectures in parsing, word embeddings, and transformers.
When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution - Part 2
Explore advanced techniques for accurate distribution inferences with limited data. Learn to de-noise empirical distributions, estimate population spectra, and recover low-rank matrix approximations.
Neural Classification with Word Embeddings - PyTorch Tutorial
Master neural classification techniques and word embeddings implementation in PyTorch through hands-on practice with essential deep learning concepts and practical applications.
Vector Semantics and Word Embeddings in Data Science
Dive into the mathematical foundations of vector semantics and word embeddings, exploring how words are represented as numerical vectors for natural language processing applications.
Vector Semantics and Word Embeddings in Natural Language Processing
Dive into vector semantics and word embeddings, exploring representation learning, distributional hypothesis, word2vec implementation, and their practical applications in natural language processing.
Introduction to Language Modeling: From N-grams to Neural Networks
Explore the evolution of language modeling, from n-grams to neural architectures, covering key concepts like embeddings, transformers, and RLHF implementations.
Word Embedding with Keras - A RStudio Tutorial
Learn word embedding techniques using Keras in RStudio. Covers theory, concepts, and practical implementation, including Keras basics and machine learning fundamentals for natural language processing.
Neural Nets for NLP 2019 - Word Vectors
Explore word embeddings, their training methods, evaluation techniques, and advanced concepts in natural language processing. Learn to represent words numerically for machine learning applications.
CMU Neural Nets for NLP 2018 - Models of Words
Explore neural network techniques for natural language processing, focusing on word models, embeddings, and evaluation methods. Learn about distributional representations and their applications in NLP systems.
CMU Advanced NLP: Bias and Fairness
Explore types of bias in NLP models, detection techniques, and mitigation strategies for fairer language processing systems.
Social Biases in Text Representations and Their Mitigation
Explore social biases in text representations and their mitigation with NLP expert Danushka Bollegala. Learn about gender bias, word embeddings, masked language models, and multi-lingual bias evaluation in AI systems.
Neural Nets for NLP 2021 - Distributional Semantics and Word Vectors
Explore distributional semantics and word vectors in NLP, covering techniques like skip-grams, CBOW, and advanced methods. Learn to evaluate, visualize, and apply word embeddings effectively.
Decoder-Only Transformers, ChatGPT's Specific Transformer, Clearly Explained
Comprehensive explanation of Decoder-Only Transformers used in ChatGPT, covering word embedding, position encoding, masked self-attention, and output generation, with comparisons to normal Transformers.
Introduction to Natural Language Processing
Explore NLP fundamentals, including language models, part-of-speech tagging, and word embeddings. Learn real-world applications and core technologies in this AI and linguistics subfield.
Transformer Neural Networks, ChatGPT's Foundation, Clearly Explained
Clear explanation of Transformer Neural Networks, the foundation of ChatGPT and modern AI. Covers key concepts like word embedding, self-attention, and encoder-decoder architecture.
Transformer Encoder in 100 Lines of Code
Learn to implement a Transformer Encoder from scratch in Python. Explore key concepts like attention mechanisms, layer normalization, and feed-forward networks through hands-on coding.
Applied Machine Learning for Startups - Arshak Navruzyan
Applied ML strategies for startups: practitioner training, team structures, guidelines, and cutting-edge techniques in NLP and deep learning.
Mathematics of Machine Learning: An Introduction - Lecture 1
Comprehensive introduction to machine learning fundamentals, covering mathematical formalization, deep learning concepts, optimization techniques, and real-world applications in various domains.
Structured Quantization for Neural Network Language Model Compression
探讨神经网络语言模型的结构化量化压缩技术,展示如何在不损失性能的情况下实现70-100倍的高压缩率,适用于资源受限场景。