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Showing 83 open source projects for "no code"
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The Ultimate Python Course
Source code and all the details for the Ultimate Python Course
...Later sections introduce object-oriented programming, inheritance, polymorphism, type hints, virtual environments, lambda functions, and functional programming tools. Each chapter is accompanied by source code and problem sets for hands-on practice. Projects include small games as well as larger applications such as a virtual assistant and an AI auto-reply bot. Additional learning materials include a Python handbook, handwritten notes, and supporting reference resources. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
...Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines of code, ktrain allows you to easily and quickly. ktrain purposely pins to a lower version of transformers to include support for older versions of TensorFlow. If you need a newer version of transformers, it is usually safe for you to upgrade transformers, as long as you do it after installing ktrain. As of v0.30.x, TensorFlow installation is optional and only required if training neural networks. -
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Mastering Bitcoin
Mastering Bitcoin 3rd Edition - Programming the Open Blockchain
The bitcoinbook repository contains the source code for Mastering Bitcoin, the authoritative open-source book by Andreas M. Antonopoulos on Bitcoin and cryptocurrency technologies. Written in a collaborative and continuously updated format using Markdown and AsciiDoc, the book serves as a comprehensive technical guide for developers, engineers, and system architects who want to understand how Bitcoin works. -
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Summarize from Feedback
Code for "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 component, and the reinforcement learning (or preference-based fine-tuning) phase. The repo also includes utilities for dataset handling, modeling architectures, inference, and evaluation. Because the codebase is experimental, parts of it may not run out-of-box depending on dependencies or environment, but it remains a canonical reference for how to implement summarization via human feedback. -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. -
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100 Days of Code YouTube
Source code for 100 days of code python course on YouTube
100 Days of Code YouTube is a day-by-day source-code collection for a comprehensive Python programming course. The repository organizes lessons into numbered folders that progress from basic concepts to advanced language features. Early material covers variables, data types, input, strings, conditions, loops, functions, and common collections. Intermediate lessons explore exceptions, modules, file I/O, functional programming, and extensive object-oriented programming concepts. ... -
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Python Mastery (Course)
Advanced Python Mastery
python-mastery is a collection of course materials created by David Beazley for teaching advanced Python programming concepts. It emphasizes deep understanding through real-world coding exercises and topics like generators, decorators, closures, and metaclasses. The repository is designed for learners who already know the basics of Python and want to push their skills to an expert level. -
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The Art of Programming
A collection of practical tips can be found at the bottom of this page
The Art of Programming (Second Edition) is a curated collection of programming problems and solutions originally derived from the Microsoft 100 Interview Questions blog series, later refined into a long-running tutorial and ultimately a published book. Created by July, the series began in 2010 and has since evolved into an in-depth exploration of algorithmic thinking, data structures, and coding interview preparation. The repository brings together 42 classic programming problems from the... -
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nanogpt-lecture
Code created in the Neural Networks: Zero To Hero video lecture
nanogpt-lecture contains the code developed during the nanoGPT installment of Andrej Karpathy's Neural Networks: Zero to Hero lecture series. It is designed to accompany the video so students can inspect, modify, and rerun the implementation themselves. The repository begins with a simple character-level bigram language model and develops toward a GPT-style transformer. -
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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. ... -
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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 easily trace how actions, rewards, and model updates connect. ... -
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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. ... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. Catalyst is compatible with Python 3.6+. ... -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing... -
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Lines
Lines a game written in Python two players through internet
...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. The game is a good example for learning pygame. -
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Muc_systray x86
A demonstration python animation system tray X86
This project show you the potential of mini meme on your taskbar. That will help to maximize your OC expose promotion and the the way interaction. Full source code accessible. -
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Python Tutorials
Machine Learning Tutorials
...It also provides tutorials on machine learning frameworks and concepts, including TensorFlow, PyTorch, Keras, Scikit-Learn, and reinforcement learning techniques. Each section contains organized code and explanations designed to help learners understand the underlying mechanics of Python and common computational approaches. -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
...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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IPython
Command shell for interactive computing in multiple languages
IPython provides a rich toolkit to help you make the most of using Python interactively. Comprehensive object introspection. IPython provides input history, persistent across sessions. Caching of output results during a session with automatically generated references. Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. Extensible system of ‘magic’ commands for controlling the environment and performing many... -
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pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
...The repository walks users through core concepts such as tensors, autograd, neural network modules, convolutional networks, recurrent networks, and transfer learning. Each section includes runnable code examples that progressively increase in complexity, helping learners build intuition while practicing hands-on implementation. Because the tutorials are concise and practical, the project is widely used in classrooms and self-study environments. Overall, it functions as both a learning curriculum and a quick reference for common PyTorch workflows. -
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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. The repository covers core neural network concepts such as weights, biases, activation functions, and gradient descent, as well as more advanced techniques like convolutional networks, recurrent networks, and reinforcement learning. It includes multiple hands-on projects, such as handwritten digit recognition, airplane detection in images, and text generation using recurrent neural networks, which demonstrate how different architectures solve real-world problems. -
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...Installation Videos! Part 1: http://youtu.be/rnv2VLcG-eI Part 2: http://youtu.be/eFudbMWHNlQ Special thanks to Wells Oliver for the code for downloading Retrosheet files. And the Chadwick project for its Retrosheet tools. https://sourceforge.net/projects/chadwick/?source=recommended
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Python4Proteomics Course
Python course for Proteomics analysis
Python course (in Spanish) for Proteomics analysis using basically Jupyter NoteBooks. For more information, you can have a look at the readme.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/p4p/code/ci/default/tree/readme.md -
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Spinning Up in Deep RL
Educational resource to help anyone learn deep reinforcement learning
Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). For the unfamiliar, reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning. At OpenAI, we believe that deep learning generally, and deep reinforcement learning specifically, will play central roles in the... -
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Neural MMO
Code for the paper "Neural MMO: A Massively Multiagent Game..."
Neural MMO is a massively multi-agent simulation environment developed by OpenAI for reinforcement learning research. It provides a persistent, procedurally generated world where thousands of agents can interact, compete, and cooperate in real time. The environment is inspired by Massively Multiplayer Online Role-Playing Games (MMORPGs), featuring resource gathering, combat mechanics, exploration, and survival challenges. Agents learn behaviors in a shared ecosystem that supports long-term...
