ChristosChristofidis/awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
https://github.com/ChristosChristofidis/awesome-deep-learning.gitSignal Finder is an independent product. Some AI-powered features use third-party AI models to generate summaries, search assistance, and research outputs. Model providers do not operate, endorse, or control Signal Finder.
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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.
吴恩达老师的机器学习课程个人笔记
This project provides comprehensive Chinese notes for Andrew Ng's Machine Learning course. It covers topics from linear regression to neural networks, including single and multiple variable linear regression, logistic regression, regularization, and more. The notes are accompanied by Octave tutorials and are available in markdown, html, and word formats for easy reading and reference.
Develop an interactive, subscription-based platform that hosts the repository's content, offering additional features such as quizzes, project templates, and a community forum. Differentiate by integrating real-world datasets and case studies for hands-on learning, and by providing expert Q&A sessions with industry professionals.
PyTorch Tutorial for Deep Learning Researchers
This repository offers a PyTorch tutorial for deep learning researchers, covering basics like linear regression and logistic regression, intermediate topics such as CNNs and RNNs, advanced topics like GANs and VAEs, and utilities like TensorBoard integration. It supports Python 2.7 or 3.5+ and PyTorch 0.4.0+.
Develop an interactive, online PyTorch learning platform that integrates the repository's tutorial code with interactive elements and real-world datasets. Target the academic and corporate training markets by offering a unique, hands-on learning experience that differentiates from traditional courses through its code-first approach and practical application of PyTorch models.
deep learning for image processing including classification and object-detection etc.
This project is a tutorial on deep learning applications in image processing, covering classification, object detection, and segmentation. It includes step-by-step guides on building and training various neural networks using Pytorch and Tensorflow, with a focus on popular architectures like LeNet, AlexNet, VggNet, ResNet, and more. The tutorials are available in video format and include source code for different platforms.
Develop an online learning platform that integrates the repository's content, offering interactive courses on image processing with Pytorch and TensorFlow. Target the niche market of AI and machine learning enthusiasts and professionals. Differentiate through hands-on labs and real-world project examples that leverage the repository's deep learning models.
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Hands-On Large Language Models provides code examples for the O'Reilly book by Jay Alammar and Maarten Grootendorst, focusing on practical tools and concepts for using LLMs. It is designed for educational purposes and supports Google Colab for easy setup with T4 GPU and 16GB VRAM.
Develop an interactive, online course that combines the repository's code examples with interactive learning modules and real-world case studies. Target the educational and corporate training markets. Differentiate by offering hands-on, practical LLM implementation guidance, leveraging the repository's code examples and workflows for text generation and data processing.