mrdbourke/pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
The 'ML-For-Beginners' project is a 12-week, 26-lesson curriculum on classic machine learning using Scikit-learn, with quizzes and projects. It supports multiple languages and is designed for self-paced learning on GitHub, with a focus on building skills through practical application.
Develop an interactive, subscription-based online learning platform that integrates the ML-For-Beginners curriculum with real-world project examples and mentorship. Differentiate by offering personalized learning paths and industry-relevant case studies.
21 Lessons, Get Started Building with Generative AI
21 Lessons teaching everything you need to know to start building Generative AI applications 🌐 Multi-Language Support Supported via GitHub Action (Automated & Always Up-to-Date) Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | C
Focus on one document-heavy or ops-heavy job and make the path from raw context to useful answer dramatically shorter.
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Build a Large Language Model (From Scratch) This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch). In *Build a Large Language Model (From Scratch)*, you'll learn and understand how large l
Build a single-purpose inference service optimized for one model family with better defaults and lower ops overhead.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
The 'llm-course' is a comprehensive guide to Large Language Models (LLMs), offering fundamental knowledge, latest techniques, and application development. It includes optional sections on mathematics, Python, and neural networks, with hands-on Colab notebooks and resources for further learning.
Build a learning platform for one skill domain with structured paths, progress tracking, and community.
Examples and guides for using the OpenAI API
The OpenAI API Cookbook provides example code and guides for using the OpenAI API, requiring an OpenAI account and API key. It includes Python examples and is applicable across various languages. The project is licensed under the MIT License.
Develop a subscription-based AI development platform that provides curated examples, tutorials, and support for the OpenAI API. Target niche markets such as educational institutions and startups by offering a unique blend of ready-to-use code snippets, comprehensive documentation, and personalized support. Differentiate from existing solutions by providing a more user-friendly interface and a focus on real-world application examples.