Creating Self-Instruct Data Sets for LLM Fine-Tuning with ChatGPT
Master the creation of synthetic instruction datasets using ChatGPT and GPT-4 for fine-tuning language models, covering data generation, multi-task training, and task decomposition techniques.
Self-Instruct Fine-Tuning of Large Language Models - Introduction to Alpaca
Master self-instruct fine-tuning techniques for LLMs, focusing on synthetic data generation using ChatGPT/GPT-4 to enhance model performance in specific tasks like summarization and translation.
Unraveling ZKML - Present Realities and Future Horizons in Privacy-Preserving AI
Explore the intersection of zero-knowledge proofs and machine learning, examining current applications, privacy challenges, and future developments in AI security.
JARVIS - OpenAI + Python Powered AI Desktop Assistant That Talks Like a Human
Build a Python-powered AI desktop assistant using OpenAI, enabling human-like conversations and task execution. Learn to create, enhance, and integrate advanced AI functionalities step-by-step.
Using MPT-7B in Hugging Face and LangChain
Explore MPT-7B LLM implementation in Hugging Face and LangChain, covering setup, initialization, tokenization, and text generation. Learn to leverage open-source models for AI applications.
Hugging Face Agents — First Look
Explore Hugging Face's new LLM Agents: easy-to-use, multi-modal AI tools integrated with HF's vast model hub. Learn implementation, querying, and potential applications in this comprehensive overview.
Build Conversational Agents with Vector DBs - LangChain
Learn to create powerful conversational agents using vector databases and LangChain, combining retrieval augmentation with chatbots for enhanced data freshness and domain-specific knowledge.
Testing Starcoder for Reasoning with PAL
Explore StarCoder's reasoning capabilities and compatibility with PAL Chain in LangChain through demonstrations, code examples, and practical tests.
StarCoder - The LLM to Make You a Coding Star
Explore StarCoder's advanced code generation capabilities, including continuous coding and gap-filling. Learn about its applications, versions, and impressive 8,000-token sequence length for enhanced coding assistance.
LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI
Explore LangChain for retrieval QA using local language models like Flan T5-XL, FastChat-T5, StableVicuna, and WizardLM, without relying on OpenAI services.
Llama Index - Step by Step Introduction
Step-by-step guide to using Llama Index for document storage, indexing, and querying with customizable LLM settings and prompt helpers. Learn to create, manage, and interact with vector store indexes efficiently.
QA Over Documents with Auto Vector Index Selection with Langchain Router Chains
Learn to automatically select vector indexes and prompts for document QA using Langchain router chains, with code demos for Streamlit and terminal applications.
Privacy and Fairness in Collaborative AI
Explore information-theoretic approaches to enhance privacy and fairness in collaborative AI systems, focusing on trustworthy machine learning techniques.
Inside ChatGPT- Unveiling the Training Process of OpenAI's Language Model
Explore the training process behind ChatGPT, including generative pretraining, supervised fine-tuning, and reinforcement learning through human feedback. Gain insights into AI language model development.
How Replit Trained Their Own LLMs - LLM Bootcamp
Comprehensive guide to training custom LLMs: from data processing and tokenization to evaluation and deployment, with insights on tools, best practices, and essential skills for LLM engineers.
Agents Masterclass from LangChain Founder - LLM Bootcamp
Explore tool-using language models with long-term memory in this comprehensive overview of AI agents, covering key concepts, challenges, and real-world examples from LangChain's co-creator.
Troubleshooting Deep Neural Networks - Full Stack Deep Learning - Spring 2021
Learn a systematic approach to troubleshoot deep neural networks, from starting simple to tuning hyperparameters, with practical strategies for implementation, debugging, evaluation, and improvement.
Transfer Learning and Transformers - Full Stack Deep Learning - Spring 2021
Explore transfer learning's evolution from computer vision to NLP, diving into embeddings, language models, and the revolutionary Transformer architecture. Gain insights into various models and their applications.
Recurrent Neural Networks - Full Stack Deep Learning - Spring 2021
Explore Recurrent Neural Networks: architecture, challenges, solutions, and applications. Dive into LSTMs, bidirectionality, attention mechanisms, CTC loss, and non-recurrent sequence models like WaveNet.
Computer Vision Applications - Full Stack Deep Learning - Spring 2021
Comprehensive overview of deep learning applications in computer vision, covering ConvNet architectures, detection methods, and advanced tasks like 3D shape inference and style transfer.
Convolutional Neural Networks - Full Stack Deep Learning - Spring 2021
Explore convolutional neural networks, from basic operations to advanced concepts like dilated convolutions and pooling. Learn about the classic LeNet architecture and gain insights into ConvNet implementation.