Search Results for "training"
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Showing 107 open source projects for "training"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Rust Training Books
Beginner, advanced, expert level Rust training material
Rust Training Books is a comprehensive educational repository created to teach developers the Rust programming language through structured lessons, exercises, and practical examples. It is designed to guide learners from basic concepts such as ownership and borrowing to more advanced topics like concurrency, performance optimization, and system-level programming. -
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Megatron-LM
Ongoing research training transformer models at scale
Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. The framework includes mixed-precision training options such as FP16, BF16, FP8, and FP4 to maximize performance and memory efficiency on modern hardware. ... -
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Comprehensive Rust
This is the Rust course used by the Android team at Google
Comprehensive Rust is an open source training course developed by Google to provide a complete introduction to the Rust programming language. Originally created for Google engineers, it has since been released publicly for the broader developer community. The course is structured into modular lessons that cover the fundamentals of Rust, including ownership, lifetimes, traits, generics, and error handling, before progressing to advanced topics like concurrency, async programming, unsafe Rust, and FFI. ... -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
...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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LLM Course
Course to get into Large Language Models (LLMs)
...Evaluation is treated as a first-class topic, with examples of automatic and human-in-the-loop methods to catch regressions and verify quality beyond simple loss values. By the end, students have a mental model and a practical toolkit for iterating on datasets, training configs, etc. -
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Anki
Anki is a smart spaced repetition flashcard program
...It supports a wide variety of media types (text, images, audio, LaTeX), advanced scheduling algorithms (SM‑2, FSRS), and extensibility via add‑ons. It’s widely used for education, language learning, medical training, and more. -
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Canvas LMS
The open LMS by Instructure, Inc.
Canvas LMS is a full-featured learning management system designed for K–12, higher-ed, and professional training, with a strong emphasis on usability and openness. Instructors build courses from modular content—pages, assignments, discussions, quizzes—and organize them into learning paths with prerequisites and due dates. Rich grading tools like SpeedGrader streamline assessment with rubrics, inline annotations, and audio/video feedback, while the gradebook supports weighting, outcomes, and late/missing policies. ... -
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Interpretable machine learning
Book about interpretable machine learning
...As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. The later chapters focus on analyzing complex models and their decisions. ... -
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LLM101n
LLM101n: Let's build a Storyteller
LLM101n is an educational repository that walks you through building and understanding large language models from first principles. It emphasizes intuition and hands-on implementation, guiding you from tokenization and embeddings to attention, transformer blocks, and sampling. The materials favor compact, readable code and incremental steps, so learners can verify each concept before moving on. You’ll see how data pipelines, batching, masking, and positional encodings fit together to train a... -
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aie-book
Resources for AI engineers
...It focuses on bridging the gap between theoretical machine learning knowledge and practical engineering workflows required to deploy AI systems in production. The material emphasizes system design, data pipelines, evaluation strategies, and deployment considerations rather than just model training. It explores how to work with large language models, retrieval systems, and agent-based architectures, providing a practical perspective on how AI is actually used in industry. The repository serves as both a learning resource and a reference, combining explanations, examples, and structured insights into a cohesive framework. It is especially useful for engineers transitioning into AI-focused roles who need to understand the full lifecycle of AI applications. -
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Stanza
Stanford NLP Python library for many human languages
...The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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Companion notebooks for Deep Learning
Jupyter notebooks for the code samples of the book
...It leverages modern libraries and frameworks to demonstrate real-world applications of deep learning techniques. The notebooks also emphasize best practices in model training, evaluation, and deployment. Overall, this project serves as a comprehensive educational resource for learning deep learning through practical experimentation. -
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DevOps Exercises
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git
DevOps Exercises is a massive, community-maintained collection of questions, tasks, and mini-challenges that cover the breadth of modern DevOps and platform engineering. It spans Linux, networking, Docker, Kubernetes, CI/CD, monitoring, cloud providers, security, and even soft skills and troubleshooting. The idea is to give candidates and teams a realistic practice ground for interviews, certifications, and day-to-day operational work. Because it’s structured as Q&A and exercises, you can go... -
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Roadmap To Learn Generative AI In 2025
Basic Machine Learning Natural Language Processing Roadmap
...The roadmap outlines recommended topics, sequential steps, and associated resources (tutorials, notebooks, project ideas) to build competence in generative modeling from conceptual understanding to implementation and deployment. By organizing the learning journey in digestible phases — from fundamentals of neural networks to deep generative architectures, and from model training to serving/inference pipelines — it reduces the cognitive load of “where to start”. -
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forma.lms
forma.lms is a Learning Management System (LMS), opensource lms
forma.lms is an open-source, web-based elearning platform (learning management system - LMS), used to manage and deliver online training courses. It's focused on corporate training needs. The projects forma.lms ((aka formalms) was originally created as a fork of Docebo CE v 4.0.5. The support comnunity of forma.Lms is active on https://forum.formalms.org/ -
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EarQuiz Frequencies
Software for technical ear training on equalization
EarQuiz Frequencies is a software for ear training on equalization. Its goal is to help musicians, audio professionals, hobbyists and students learn how to hear frequency bands. Available for Windows 10, 11 (x64), macOS 11 or higher (both for Intel and Apple Silicon) and Linux. This application is based on (and deeply inspired by) the world-renowned Golden Ears method of David Moulton, whose course is half dedicated to building this essential critical listening skill. -
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Fuzzy machine learning framework
A library and a GUI front-end for fuzzy machine learning
Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent... -
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Digital Forensics Guide
Learn all about Digital Forensics and Computer Forensics
The Digital Forensics Guide repository is a comprehensive, structured reference for investigators, analysts, students, and cybersecurity professionals interested in digital forensics principles, tools, methodologies, and workflows. It organizes foundational topics such as evidence acquisition, disk and memory analysis, file system structures, network forensics, artifact extraction, timeline generation, and reporting into digestible modules that help build core competency. Alongside... -
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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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Flutter Tutorials
Source code for all the tutorials on FilledStacks' channel
...Tutorials often come with detailed commentary or videos to explain design decisions. The collection helps developers bridge the gap between learning Flutter basics and building production-ready apps. It serves as both a reference and a training resource for those aiming to adopt Flutter in professional projects. -
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Flash Card Tutor
Randomly display language quiz lines from a text file
...The Quiz and Answer lines are derived from a file. The file is organized with Quiz lines on an even numbered line, and the Answer lines on odd-numbered lines, as shown in this example for Thai language training: Hello [-- on line #0 --) Saw wat dee kup [-- one line #1--] Excuse me [-- on line #2 --] Kho thoet kup [-- on line #3 --] I speak Thai a little [-- on line #4 --] Phom phut paasaa Thai dai nitnoi kup [-- on line #5 --] (Another even-numbered quiz line) (Another odd-numbered answer line, etc.) -
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haskell-trainings
Haskell 101 and 102: slides and codelabs
...It covers a wide range of topics from beginner to advanced, including functional programming principles, monads, type classes, concurrency, and performance. The repository is designed to support self-paced learning or instructor-led training and reflects Google's internal education efforts to promote functional programming skills. -
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data-science-on-gcp
Source code accompanying book: Data Science on the GCP
...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. It emphasizes practical, production-oriented workflows rather than isolated examples, showing how different Google Cloud services interact to form cohesive pipelines. -
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AllenNLP
An open-source NLP research library, built on PyTorch
AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional... -
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Reinforcement Learning Methods
Simple Reinforcement learning tutorials
...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. The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. By using TensorFlow as the backbone, it highlights practical considerations such as tensor shapes, loss computation, optimization steps, and batching in an RL context.
