Search Results for "teaching" - Page 2
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Showing 68 open source projects for "teaching"
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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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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
...The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. Because the code is intentionally lightweight, it is often used as a teaching resource for students learning about natural language processing and deep learning architectures. Developers can explore the repository to understand how language models generate text and how transformer components interact within the architecture. -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
...By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is great for customization and teaching purposes. -
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d2l-zh
Chinese-language edition of Dive into Deep Learning
...It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning. -
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Dummy-Robot
My super mini robotic arm robot project
Dummy-Robot is an open source robotics project by Peng Zhihui that showcases the design and implementation of a compact robotic arm . The repository contains full hardware design files, firmware, control software, and 3D models. It includes components such as a gripper, LED light ring PCB, wireless spatial positioning controller, and a portable case. The project demonstrates advanced engineering with stepper motor drivers, custom controllers, and debugging tools. While the original version... -
6
Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. -
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Padasip
Python Adaptive Signal Processing
...It includes a variety of adaptive filter algorithms such as LMS, RLS, and their variants, offering real-time adaptation to changing environments. The library is lightweight, well-documented, and ideal for research, prototyping, or teaching purposes. Padasip supports both supervised and unsupervised filtering modes and is built to be modular and extensible, making it easy to integrate into larger machine learning pipelines or control systems. -
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Deep-Learning-with-TensorFlow-book
Open source Deep Learning book, based on TensorFlow
Deep-Learning-with-TensorFlow-book is an open-source deep learning book based on TensorFlow 2.0. It combines theory with practical examples, making it suitable for beginners who want to learn deep learning through hands-on code. The repository includes the PDF book, companion source code, course slides, and notebook-style materials. It covers core machine learning and deep learning concepts through TensorFlow-based implementation. The project also supports educational use, with materials... -
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Etchimaths (ALL categories)
This is a maths solving app and study guide for various levels..
Etchimaths is a Mathematics software, a helping tool for Mathematics simulation(solving) and study.It is a solving as well as a teaching software. It involves Mathematics programs at various levels of studies;they include: 1.Etchimaths(O'LEVEL) 2.Etchimaths(A'LEVEL) 3.Etchimaths(UNIVERSITY) 4.Etchimaths(BEPC/BEPC+1) 5.Etchimaths(BAC) 6.Etchimaths(UNIVERSITE) 7.Etchimaths(ULTIMATE) and more to come in future.. -
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PS-Drone
Programming a Parrot AR.Drone 2.0 with Python - The Easy Way
...It was designed to be easy to learn, but it offers the full set of the possibilities of the AR.Drone 2.0, including Sensor-Data (aka NavData), Configuration and full Video-support. The video function is not restricted to mere viewing, it is also possible to analyze video images data using OpenCV2. Obviously, the PS-Drone is perfect for teaching purposes; however, even the requirements for professional purposes can be satisfied. PS-Drone comes with a tutorial, explaining its most important commands and the drone's most important sensor values. The examples are easy to understand for people with little programming experience. A full list of commands and a description of all sensor data is available in a detailed documentation. ... -
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Mininet
Emulator for rapid prototyping of Software Defined Networks
...Because you can easily interact with your network using the Mininet CLI (and API), customize it, share it with others, or deploy it on real hardware, Mininet is useful for development, teaching, and research. Mininet is also a great way to develop, share, and experiment with Software-Defined Networking (SDN) systems using OpenFlow and P4. Mininet is actively developed and supported, and is released under a permissive BSD Open Source license. We encourage you to contribute code, bug reports/fixes, documentation, and anything else that can improve the system! ... -
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SkyWater PDK
Open source process design kit for usage with SkyWater Technology
...Standard-cell libraries and IO pads are included so digital designers can use open synthesis and place-and-route to reach a manufacturable GDS. Because the PDK is open, it becomes a common target for community reference designs, open tapeouts, and teaching curricula. Documentation and example flows show how to assemble complete toolchains, from RTL to sign-off, using open EDA components. The project effectively lowers the barrier to custom silicon, catalyzing an ecosystem around an accessible 130-nm process. -
13
Spinning Up in Deep RL
Educational resource to help anyone learn deep reinforcement learning
...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 development of powerful AI technology. To ensure that AI is safe, we have to come up with safety strategies and algorithms that are compatible with this paradigm. ... -
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weatherPi
An extensible weather sensor recording network, based on Raspberry Pis
A distributed network of sensors with a central data store, to record environmental data. Project goals include: * to serve as a teaching outlet for: ** sensor programming ** distributed data collecting ** web APIs ** database ** web UI for numerical data ** graphing data sensibly It also gives me some infotainment in new (to me) technologies, and as an excuse to scatter ever more Raspberry Pis around my home. -
15
Scikit-learn Tutorial
An introductory tutorial for scikit-learn
...The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. Although the GitHub repository has been archived and is read-only, it is still a valuable snapshot of early, hands-on teaching material for scikit-learn and machine learning in Python. -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
...Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. By combining narrative explanations with executable code, it shortens the path from theory to working prototypes. -
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Syllog
Syllog is a system for teaching logic, with anonymous answer logging
Syllog is a system for teaching various forms of logic, including syllogistic logic. It consists of a student-facing Java Applet that runs in the student's browser, and a teacher-facing, server-based logging facility for recording the answers given by students. The student-facing part simply poses questions, to which the student must provide an answer. The teacher-oriented logging facility then logs the answers anonymously for further investigation by the teacher. -
18
PyTorch Book
PyTorch tutorials and fun projects including neural talk
...The basic part (the first five chapters) explains the content of PyTorch. This part introduces the main modules in PyTorch and some tools commonly used in deep learning. For this part of the content, Jupyter Notebook is used as a teaching tool here, and readers can modify and run with notebooks and repeat experiments. -
19
Of Mages and Magic
A Simple RPG fighting game for teaching Python
...These mages can be organised into teams which fight each other until only one team remains. The game is currently in early alpha, meaning some aspects of the interface are a bit clunky. However, in its current iteration, it is fairly suitable for teaching. Any and all feedback would be greatly appreciated -
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Etchimaths(A'LEVEL)
Etchimaths(A'LEVEL) is a typical Mathematics software for A'LEVEL
Etchimaths(A'LEVEL) is a Mathematics software, a helping tool for Mathematics simulation(solving) and study for the GCE ADVANCED LEVEL standard.It is a solving machine as well as a teaching machine -
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Question Answering Corpus
Question answering dataset in "Teaching Machines to Read & Comprehend"
RC-Data is a dataset generation framework created by Google DeepMind to produce large-scale reading comprehension question-answer pairs from CNN and Daily Mail news articles. The dataset, introduced in the 2015 paper “Teaching Machines to Read and Comprehend” (Hermann et al., NIPS 2015), was among the first large corpora designed to train and evaluate machine reading and comprehension models. The repository provides scripts for downloading archived CNN and Daily Mail articles from the Wayback Machine and automatically generating cloze-style questions where entities in the text are replaced with placeholders. ... -
22
WavePacket
Dynamics of quantum systems, controlled by external fields
...External electric fields can be added within the semiclassical dipole approximation, thus WavePacket can be used to simulate modern experiments involving ultrashort light pulses in photo-induced physics or chemistry, including quantum optimal control. With its visualization of quantum dynamics generated 'on the fly', WavePacket is suitable for teaching quantum mechanics as well as for research projects, see also the numerous demonstration examples . The Matlab/Octave version is mature and has been used in production for decades. The C++/Python version has been superseded by https://github.com/ulflor/wavepacket. Using tensor train techniques, the additional WaveTrain package aims at beating the curse of dimensionalit -
23
Import from github_com
Python module finder/loader from github, like in golang
...It hooks into Python’s import machinery so that when you reference a GitHub path, it fetches the code, stores it locally (often with caching), and makes it importable in the current environment. This is helpful for prototyping, teaching, or quickly exploring other people’s utilities without polluting your global site-packages. It also demonstrates how flexible Python’s import system is and can be used as a reference for custom importers. While not a substitute for proper packaging, it’s a clever shortcut for rapid experimentation. -
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Fluid2D
Fluid2D is the Swiss army knife of 2D CFD
Fluid2D allows to study a wide variety of 2D flows. It is written entirely in Python. It is both a teaching code and a research code, capable of running from one core to thousands. Its numerics has been chosen to yield to as small dissipation as possible allowing to simulate easily high Reynolds flows, much higher than any of its concurrents. High performances are achieved by writting most of the operations as matrix-vector multiplications, handled by numpy that itself relies on the highly optimized BLAS. -
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simplex with branch and bound
A tool for teaching simplex and branch & bound methods
This is a tool for teaching simplex and branch & bound methods. For simplex method, it comes with several examples including degeneracy and cycling, and allow the user to dictate how to pivot. For branch and bound method, it is desinged to interact with the user to explore all possible branch and bound trees. The user can also load a problem from a text file or simply type in a model directly.
