Search Results for "deep learning toolbox"
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Showing 24 open source projects for "deep learning toolbox"
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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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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. 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. ... -
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Recommenders
Best practices on recommendation systems
The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several... -
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The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. The repository bundles tutorials, lecture notes, project outlines, course materials, and references across topics like Python, statistics, ML algorithms, deep learning, NLP, data preprocessing, model evaluation, and real-world problem solving. ... -
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PythonPark
Python open source project "The Road to Self-Study Programming"
PythonPark is a large, curated “learning playground” for Python — essentially a comprehensive self-study meta-repository aimed at helping learners progress in Python programming, data science, machine learning, web scraping, and software engineering practices. It aggregates tutorials, learning guides, project examples, and resources across topics: from Python basics and data structures to machine learning, web scraping, and even interview preparation and “programmer life” guidance. Because... -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The... -
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Linux insides
A book-in-progress about the Linux kernel and its insides
...It is written for readers who already have some familiarity with C and assembly language and want to understand what happens under the hood of Linux. The material is continuously updated as the kernel evolves, reflecting changes in modern kernel versions. Overall, linux-insides is widely regarded as a deep technical learning resource for systems programmers and advanced Linux enthusiasts. -
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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. ... -
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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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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. -
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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. -
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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. -
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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. ... -
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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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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. -
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pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
pytorch-tutorial is a highly popular educational repository that teaches deep learning with PyTorch through step-by-step examples and well-structured lessons. It is designed primarily for beginners and intermediate practitioners who want to understand PyTorch fundamentals and quickly move toward building real neural network models. The repository walks users through core concepts such as tensors, autograd, neural network modules, convolutional networks, recurrent networks, and transfer learning. ... -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. -
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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 development of powerful AI technology. ... -
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A Machine Learning Course with Python
A course about machine learning with Python
The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python. Machine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built... -
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jieba
Stuttering Chinese word segmentation
...The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable for word segmentation in search engines. The paddle mode uses the PaddlePaddle deep learning framework to train the sequence labeling (bidirectional GRU) network model to achieve word segmentation. Also supports part-of-speech tagging. To use paddle mode, you need to install paddlepaddle-tiny, pip install paddlepaddle-tiny==1.6.1. Currently paddle mode supports jieba v0.40 and above. For versions below jieba v0.40, please upgrade jieba, pip install jieba --upgrade. -
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The Deep Review
A collaboratively written review paper on deep learning, genomics, etc
This repository is home to the Deep Review, a review article on deep learning in precision medicine. The Deep Review is collaboratively written on GitHub using a tool called Manubot (see below). The project operates on an open contribution model, welcoming contributions from anyone. To see what's incoming, check the open pull requests. For project discussion and planning see the Issues. -
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stanford-tensorflow-tutorials
This repository contains code examples for the Stanford's course
This repository contains code examples for the course CS 20: TensorFlow for Deep Learning Research. It will be updated as the class progresses. Detailed syllabus and lecture notes can be found in the site. For this course, I use python3.6 and TensorFlow 1.4.1. -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
...The explanations are present in the wiki associated with this repository. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. ... -
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Convolution arithmetic
A technical report on convolution arithmetic in deep learning
A technical report on convolution arithmetic in the context of deep learning. The code and the images of this tutorial are free to use as regulated by the licence and subject to proper attribution. The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory. We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. ...
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