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An introductory machine learning course taught by Caltech Professor Yaser Abu-Mostafa, recorded from a live broadcast and available as a free online MOOC. It covers basic theory, algorithms and applications across 18 lectures, with homework sets and a final exam. Prerequisites are basic probability, matrices and calculus.
Tutorial teaching the main ideas of unsupervised feature learning and deep learning, with implementation of feature learning and deep learning algorithms. It assumes basic machine learning knowledge, including supervised learning, logistic regression and gradient descent, and suggests completing an earlier machine learning course first.
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.
Animated AI is a project of animations and instructional videos about neural networks. Its topics cover convolution basics, padding, stride, grouped and depthwise-separable convolution, and pixel shuffle. It is licensed under the MIT License.
Apache Zeppelin is a web-based notebook for data-driven, interactive data analytics and collaborative documents with SQL, Scala, Python, R and more. It uses interpreters to plug in language and data-processing backends, including built-in Apache Spark integration, and includes basic charts, pivot charts, dynamic forms and paragraph publishing to embed results in websites.
Basic functions for clustering data: k-means, dp-means, etc.
Colibri Core is an NLP tool and a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams, i.e. patterns with one or more gaps of fixed or dynamic size. It counts and extracts patterns from large corpus data. The central tool, colibri-patternmodeller, builds, views, manipulates and queries pattern models.
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.
A GitHub Repository Where you can Learn Datavisualizatoin Basics to Intermediate level.
Basic proof of concept for genetic architecture search in Keras.
Materials for the Hugging Face Diffusion Models Course, a free course on diffusion models. It covers theory, generating images and audio with Diffusers, training and fine-tuning models, and conditional generation. Prerequisites include Python skills and basics in deep learning and PyTorch.
II-Commons-Skills provides fast, deterministic retrieval across arXiv, PubMed / PMC and supported US policy corpora, distributed as an npm package and CLI requiring Node.js 18 or newer. Basic usage works without authentication, and an API token is available for higher usage limits. It is licensed under Apache-2.0.
imutils is a Python package of convenience functions that make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV. It works with Python 2.7 and Python 3 and is pip-installable given NumPy, SciPy, Matplotlib and OpenCV. A find_function method searches function names across modules.
A course on generative modelling, currently unfinished, with the first approximately five lessons mostly done. It covers topics such as PyTorch basics, representations, CLIP, GANs, sequence modelling and transformers. No prior deep learning knowledge is assumed, though comfort with some Python and Jupyter Notebooks is expected.
A Clojure wrapper for deeplearning4j with added syntactic sugar, aiming to provide a declarative API for machine learning on the JVM. It translates Clojure keywords into Java method calls, making many deeplearning4j configuration options accessible. The project is a work in progress, and configurations have been tested on basic feedforward nets, RNNs and CNNs.
Kaito is the Kubernetes AI Toolchain Operator, an operator suite that automates LLM inference, fine-tuning and RAG engine deployment in a Kubernetes cluster. It simplifies the CRD API and provides optimized preset configurations for inference engine scheduling parameters. Workspaces are the basic CRD for managing inference and tuning workloads, and all vLLM-supported models can be run.
A beginner-friendly guide on using Keras to implement a simple neural network in Python. It walks through MNIST handwritten digit classification, covering setup, data preparation, building the model, compiling, training, testing and using the model. It is intended for complete beginners to Keras who have a basic background knowledge of neural networks.
A guide that introduces data processing and cleaning in JavaScript, teaching how to load, modify and transform data using JavaScript libraries. It covers tasks geared around preparing data for further analysis and visualization, demonstrated with core JavaScript API, d3.js and lodash. It assumes some basic knowledge of JavaScript.
Makeayo is a desktop application for generative art, offering base selection, prompt-based image generation, image transformation, HD upscaling, pose matching and access to many models. It supports Windows 10 and 11, with Mac and Linux versions described as coming soon. The site states a free tier and paid monthly founder, basic, pro and max plans.
Manifold is a package for manipulating manifolds, built for Torch7. It is installed via luarocks and depends on the libatlas3-base package. The package offers basic functions such as computing neighbors and distances, and embeddings using random projections, LLE and tSNE. Demos are included, such as a t-SNE map produced on 5,000 MNIST digits.
Mazmazika is an online music tool platform that integrates multiple music-related functions. Its tools include vocal remover, chord analyzer, ear training, beat maker, sheet music editor, piano, metronome, audio and MIDI piano visualizer, and chord library, with free basic use available in the browser.
nbshare.io is a platform for learning data analytics using Python and R. It hosts a collection of notebooks covering topics including artificial intelligence, machine learning, basic Python coding, data analytics, web scraping and other related technical subjects.
A course by Andrej Karpathy on building neural networks from scratch in code, starting with backpropagation basics and building up to modern deep neural networks like GPT. Prerequisites are solid Python programming and intro-level math such as derivatives and gaussians.
The repository holds materials and IPython notebooks for "Python for Data Analysis, 3rd Edition" by Wes McKinney, published by O'Reilly Media. Notebooks cover chapters on Python basics, NumPy, pandas, data loading, cleaning, visualization, time series and modeling libraries, plus appendices. The code is released under the MIT license.
