Search Results for "machine learning python" - Page 3
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Showing 166 open source projects for "machine learning python"
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Summarize from Feedback
Code for "Learning to summarize from human feedback"
The summarize-from-feedback repository implements the methods from the paper “Learning to Summarize from Human Feedback”. Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling... -
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Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. -
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Project Based Learning
Curated list of project-based tutorials
project-based-learning is a community-curated open source repository that compiles programming tutorials focused on building real-world applications from scratch. It organizes resources by programming languages such as Python, Java, JavaScript, C++, Go, Rust, and many others. Each tutorial emphasizes practical, hands-on learning through project development rather than theoretical study. -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to... -
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Guia do Desenvolvedor Back-end
Everything you need to become a back-end developer
...The guide covers Linux, Git, GitHub, HTTP, APIs, programming languages, databases, cloud platforms, Docker, architecture patterns, and related technical areas. It also includes resources for data science, machine learning, artificial intelligence, and scientific Python tools. The repository is organized as a study companion, not as an executable software package. Overall, it is a practical back-end learning reference for planning study paths, exploring technologies, and finding useful external resources. -
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ThinkJulia.jl
Port of the book Think Python to the Julia programming language
ThinkJulia.jl is an open source educational project that adapts Think Python by Allen B. Downey into the Julia programming language, with contributions by Ben Lauwens. It provides a comprehensive introduction to programming and computational thinking using Julia’s modern, high-performance features. The book is structured to gradually teach core concepts such as variables, control flow, functions, recursion, object-oriented programming, and data structures, while offering hands-on exercises... -
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The Art of Programming
A collection of practical tips can be found at the bottom of this page
...In July 2023, work on the second edition was announced, which expands the project with updated content, new problems inspired by recent big-tech interviews, and introductions to modern machine learning techniques such as XGBoost, CNNs, RNNs, and LSTMs. This collection serves both as a historical record of algorithm problem-solving and as a living resource for programmers preparing for interviews. -
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data-science-on-gcp
Source code accompanying book: Data Science on the GCP
The data-science-on-gcp repository is a comprehensive collection of code examples and end-to-end workflows that accompany the book Data Science on the Google Cloud Platform, designed to teach developers how to build scalable data science and machine learning systems using Google Cloud services. It provides structured, chapter-aligned implementations that guide users through the full lifecycle of a data science project, including data ingestion, storage, processing, analysis, model training, and deployment. The repository is organized into multiple directories that reflect real-world pipelines, such as ingesting data, running SQL-based analytics, streaming data processing, using Spark and Dataproc, applying BigQuery ML, and deploying models with Vertex AI. ... -
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DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
Educational is an open collection of interactive tutorials created by Google DeepMind to make the fundamentals of machine learning and artificial intelligence accessible to learners of all backgrounds. The repository provides hands-on, beginner-friendly resources that introduce essential AI concepts through Google Colab notebooks, combining intuitive explanations with executable code. The tutorials cover a broad range of topics—from foundational Python programming and data handling to supervised, unsupervised, and reinforcement learning, as well as graph neural networks and scientific reasoning. ... -
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Reinforcement Learning Methods
Simple Reinforcement learning tutorials
Reinforcement-Learning-with-TensorFlow is an educational repository that walks through key reinforcement learning algorithms implemented in TensorFlow. It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can... -
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Hello AI World
Guide to deploying deep-learning inference networks
Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You’ll also get to code your own easy-to-follow recognition program in Python or C++, and train your own DNN models onboard Jetson with PyTorch. ... -
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Catalyst
Accelerated deep learning R&D
...Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving). -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The... -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
...For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
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Lines
Lines a game written in Python two players through internet
Lines is an old game that I had programed in python (pygame). I usually prefer to use visual c# as a programing language. However, I wrote this game in python in a time period, I was learning python. Here, I want to thank all people who have training videos in youtube, they helped me a lot to make this program. Some of the code of the program is from these videos. The game can be played from one or two persons through internet. -
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Deep Learning 500 Questions
500 Questions on Deep Learning using a question-and-answer format
DeepLearning-500-questions is a comprehensive handbook that compiles 500 important questions on deep learning, curated to serve as a valuable reference for AI engineer interviews and self-study. Edited by Tan Jiyong with contributions from Guo Zizhao, Li Jian, and Dian Songyi, the book systematically covers both theoretical foundations and practical applications of deep learning. The first sections focus on essential mathematics, machine learning basics, and deep learning foundations, establishing the groundwork for more advanced topics. ... -
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python-tutorial
Practical Python tutorials, including Python basics
python-tutorial is a practical Python learning repository that collects examples for everyday programming tasks. It covers Python fundamentals, advanced language features, object-oriented programming, multithreading, databases, data science, Flask development, web crawling, and utility scripting. The project is intended for beginners learning Python and for working developers who want reference implementations for common scripts. -
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ml-design-patterns
Source code accompanying O'Reilly book: Machine Learning Design
The ml-design-patterns repository contains the source code and examples that accompany the book “Machine Learning Design Patterns,” providing practical implementations of reusable solutions for common challenges in machine learning systems. It organizes patterns into categories such as data representation, problem framing, and model training, helping practitioners understand how to structure ML pipelines effectively. The repository includes implementations of techniques like feature hashing, embeddings, feature crosses, and multimodal inputs, which are essential for handling diverse data types. ... -
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Stats With Julia Book
Collection of runnable Julia code examples for a statistics book
StatsWithJuliaBook is the companion code repository for the book Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. It contains over 200 code blocks that correspond to the book’s ten chapters and three appendices, covering topics from probability theory and data summarization to regression analysis, hypothesis testing, and machine learning basics. The repository is designed for Julia users and provides ready-to-run examples that reinforce theoretical concepts with practical implementation. ... -
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Lucid
A collection of infrastructure and tools for research
Lucid is a collection of infrastructure and tools for research in neural network interpretability. Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support. Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Collaboratory. It's a Jupyter notebook environment that requires no setup to use and runs... -
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Ceka
Crowd Environment and its Knowledge Analysis
A knowledge analysis tool for crowdsourcing based on Weka. We also have a Python version of Crowdsourcing Learning: CrowdwiseKit on GitHub (https://github.com/tssai-lab/CrowdwiseKit). -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
...This repository covers essential building blocks like sessions (for older TF versions), placeholders, variables, activation functions, and optimizers, before guiding learners through building end-to-end models for regression, classification, and data pipelines. Beyond the basics, the project includes examples of convolutional neural networks, recurrent networks, autoencoders, reinforcement learning, generative adversarial networks, and transfer learning workflows. By pairing code examples with conceptual explanations, the tutorials make abstract machine learning ideas accessible and encourage experimentation with TensorBoard visualization and distributed training. -
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wav2letter++
Facebook AI research's automatic speech recognition toolkit
First, install Flashlight (using the 0.3 branch is required) with the ASR application. This repository includes recipes to reproduce the following research papers as well as pre-trained models. All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run... -
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Tensorflow and deep learning
A crash course in six episodes for software developers
Tensorflow and deep learning repository is an educational deep learning crash course designed to help software developers quickly understand and apply machine learning concepts without requiring advanced academic background. It is structured as a series of guided lessons that combine theoretical explanations, practical examples, and runnable code, allowing learners to build intuition while actively experimenting with models.
