ChatGPT Teaches Numpy - Python Numpy Tutorial
Comprehensive tutorial on NumPy: from basics to advanced concepts. Learn array creation, manipulation, mathematical operations, reshaping, and integration with Python data structures.
GPT-4 API with Streaming Responses, Chat History, Streamlit, Gradio UI, JavaScript, FastAPI Webapp
Comprehensive tutorial on implementing GPT-4 API with streaming, chat history, and various UI options. Learn to build interactive AI-powered applications using Streamlit, Gradio, and FastAPI.
GPT-4 Codes Animations with JavaScript and Python Pygame
Learn to create HTML canvas animations with JavaScript and convert them to Python Pygame, guided by GPT-4. Explore increasing complexity in coding and witness AI's capabilities in cross-language programming.
Langchain Summary and QA with Chromadb Using OpenAI Embeddings and GPT-3 With Token Count
Build 5 Langchain apps for document summarization and Q&A using Chromadb, OpenAI embeddings, and GPT-3. Learn token counting, custom prompts, and various chain types for efficient text processing and analysis.
A Lip Sync Expert Is All You Need for Speech to Lip Generation in the Wild
Learn to generate realistic lip movements from speech using advanced AI techniques, exploring models, evaluation methods, and real-world applications in this cutting-edge field.
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition
Explore innovative techniques for improving speech recognition using reinforcement learning-based speech enhancement, covering key concepts and potential future advancements.
Meaning Representation for Natural Language Understanding - Mariana Romanyshyn - ODSC Europe 2019
Explore abstract meaning representation in NLP, its algorithms, and applications. Learn about AMR graphs and their potential to enhance question answering, summarization, and text simplification.
Fake News Classifier with Machine Learning and NLP - Part 1
Learn to build a fake news classifier using machine learning and NLP techniques. Covers data preprocessing, feature extraction, and multinomial classification algorithms for detecting misinformation.
CMU Neural Nets for NLP - Model Interpretation
Explore model interpretation in neural networks for NLP, covering themes, analysis techniques, and evaluation methods for understanding and explaining neural models.
Neural Nets for NLP - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP using local independence assumptions and conditional random fields. Learn about sequence labeling, globally normalized models, and advanced training techniques.
Neural Nets for NLP 2019 - Reinforcement Learning
Explore reinforcement learning in NLP, covering policy gradient, REINFORCE, value-based methods, and applications in dialogue and information retrieval.
Neural Nets for NLP - Structured Prediction Basics
Explore structured prediction in NLP, covering perceptron algorithms, max-margin objectives, and exposure bias remedies. Learn techniques for modeling output interactions and training globally normalized models.
Neural Nets for NLP - Debugging Neural Nets
Practical strategies for identifying and resolving issues in neural networks for NLP, covering training and test-time problems, model architecture, and data analysis.
Neural Nets for NLP 2019: Sentence and Contextualized Word Representations
Explore sentence representations and contextual word embeddings for NLP tasks. Learn about multi-task learning, transfer learning, and pre-training techniques to improve model performance.
Neural Nets for NLP 2019 - Attention
Explore attention mechanisms in neural networks for NLP, covering basic concepts, improvements, specialized varieties, and a case study on the Transformer model.
Neural Nets for NLP - Recurrent Networks for Sentence or Language Modeling
Explore recurrent networks, LSTMs, and their applications in sentence modeling and NLP. Learn about vanishing gradients, pre-training, and the strengths and weaknesses of recurrent models.
Neural Nets for NLP 2019 - Convolutional Neural Networks for Language
Explore convolutional neural networks for NLP, covering bag-of-words, context windows, sentence modeling, stacked and dilated convolutions, and structured convolution for sentence pairs.
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.
Neural Nets for NLP 2018 - Learning from-for Knowledge Graphs
Explore neural network techniques for knowledge graph construction, relation extraction, and embedding refinement in natural language processing.
CMU Neural Nets for NLP 2018 - Document-Level Models
Explore document-level NLP models, covering language modeling, topic modeling, coreference resolution, and discourse parsing. Learn advanced techniques for analyzing and processing longer texts.
Neural Nets for NLP 2018 - Models of Dialogue
Explore models of dialogue in neural networks, covering generation-based approaches, discourse-level coherence, diversity promotion, evaluation methods, and personality infusion in conversational AI.