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Org profile for Stable Diffusion concepts library on Hugging Face, the AI community building the future.
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A newsletter post by Jon Stokes introducing the concepts behind AgentGPT, BabyAGI, LangChain and LLM-powered agents. It covers core agent concepts, how LLMs make plans, chain of thought reasoning, action planning, and example prompt-response pairs.
bishopbook.com
A book offering a comprehensive introduction to central ideas underpinning deep learning, intended for newcomers to machine learning and those already experienced in the field. It covers concepts relating to contemporary architectures and techniques and includes a self-contained introduction to probability theory. Hardback, PDF eBook, and a free-to-use online version are listed as access options.
github.com
cunn is a GitHub repository under the Torch organization that provides resources for the cunn project. The repository includes documentation for installation, usage, running unit tests, and GPU training concepts.
github.com
A GitHub repository from dair-ai containing explanations of key concepts in machine learning. The README lists papers with dates and descriptions, including the Transformer, Elmo, Marian MT, GPT, BERT and Transformer XL.
datascientists.com
A membership program with a Udemy course on basic data concepts, weekly knowledge jams, career development tools and mentorship services. A limited-time membership is offered at $50 USD per year, cancellable anytime, and participants can join a LinkedIn community.
colab.research.google.com
Train custom concepts from input images with this simplified DreamBooth colab.
getleon.ai
Leon is an open-source personal assistant that can run on a user's server. It is built on Node.js, Python and artificial intelligence concepts, and responds to requests. It uses a modular architecture for creating or using skills, and supports several cloud and offline text-to-speech and speech-to-text solutions.
machinelearningmastery.com
Machine Learning Mastery is a website that provides educational content such as tutorials, quick start guides, and eBooks to help developers learn and apply machine learning concepts and algorithms. It covers topics like deep learning, computer vision, natural language processing, and specific libraries including TensorFlow, PyTorch, and scikit-learn.
mlops-guide.github.io
A guide explaining MLOps concepts and how to build an MLOps environment. It covers theory and a tutorial using tools such as DVC, CML, IBM Watson Machine Learning, IBM Watson OpenScale, Terraform and Cookiecutter. The guide is described as modular, allowing tools to be swapped according to project preferences and use cases.
mlu-explain.github.io
MLU-Explain is an education initiative from Amazon that teaches machine learning concepts through visual essays. It publishes interactive articles on topics such as neural networks, logistic regression, reinforcement learning, ROC and AUC, cross-validation, random forests, and decision trees.
github.com
modAL is a modular active learning framework designed for use with the Python programming language. The project includes introductory documentation, overviews of active learning concepts, usage examples, and quickstart guides. It is hosted as an open source repository on GitHub.
neuralnetworksanddeeplearning.com
Neural Networks and Deep Learning is a free online book by Michael Nielsen. It teaches core concepts of neural networks and deep learning, covering handwriting recognition, backpropagation, and improving how neural networks learn. The work is licensed for copying and sharing but not for selling.
nn-visual.com
NN Visual is an interactive web-based neural network visualizer. It lets users configure hidden layers, select datasets such as auto_mpg, iris, xor, or mnist, and train a network while observing forward and backpropagation step by step. The tool displays training loss, mean absolute error, weight distributions, and node activation brightness. Written explanations accompany the visualizations to teach concepts like gradient descent, weights, biases, and activation functions without requiring code.
nostarch.com
A hands-on, project-based introduction to programming with Python by Eric Matthes, published in its third edition in December 2022. The book covers basic concepts such as variables, lists, classes, and loops, and guides readers through projects including a Space Invaders-inspired arcade game, data visualizations, and a simple web application.
mcsp.wartburg.edu
Teaching page by John M. Zelle, Ph.D., for an introductory computer science textbook written in Python and published by Franklin, Beedle & Associates. It links edition-specific public resources for four editions and offers graphics.py, a simple graphics library released under the GPL for introducing object-oriented concepts. Instructor resources including problem solutions and exam questions are available from the publisher.
spreadsheets-are-all-you-need.ai
SAAYN (Spreadsheets are all you need.ai) is a platform that teaches artificial intelligence topics using familiar spreadsheet tools. It launched with a project implementing the GPT-2 model in Excel to demonstrate that AI concepts can be understood with accessible tools, and its core mission is to make complex AI topics easy to understand without oversimplifying them.
sdtools.org
SDTools is a mini wiki and cheat sheet for Stable Diffusion users. It addresses common challenges in generating desired AI images, provides brief explanations of relevant concepts, and points to helpful tools and resources. It recommends Fooocus for users new to Stable Diffusion and SD.Next for those seeking more control without manual interface adjustments.
token-verse.github.io
TokenVerse is a method for multi-concept personalization that uses a pre-trained text-to-image diffusion model. It can disentangle complex visual elements and attributes from a single image and combine concepts extracted from multiple images, supporting objects, accessories, materials, pose and lighting. It uses a DiT-based text-to-image model and learns modulation-space directions for each word.
wordasimage.github.io
Word-As-Image for Semantic Typography is a research method that automatically creates word-as-image illustrations, visualizing a word's meaning while preserving readability. It optimizes letter outlines guided by a pretrained Stable Diffusion model and handles a large variety of semantic concepts and any font.