Search Results for "deep learning"
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Showing 45 open source projects for "deep learning"
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Quality and compliance software for growing life science companiesAutomate gap analysis across FDA, ISO 13485, MDR, and 28+ regulatory standards. Cross-map evidence once, reuse across submissions. Get real-time risk alerts and board-ready dashboards, so you can expand into new markets with confidence
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The Cloud Sales Acceleration PlatformStreamline and automate your cloud sales cycle, enhance operational efficiency, and capitalize on marketplace opportunities with the Clazar Cloud Sales Acceleration Platform.
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1
Companion notebooks for Deep Learning
Jupyter notebooks for the code samples of the book
...The material is designed to be accessible while still covering advanced topics, making it suitable for both beginners and intermediate practitioners. 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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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. ... -
Striven | All In One Business Management SoftwareStriven is the all-in-one business management software that lowers your costs, improves your operations, and makes work easier. Make your company’s data coherent, connected, and relevant.
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Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
Perfect Roadmap To Learn Data Science In 2025 is an extended, updated learning pathway curated for the modern data-science landscape — blending classical data-analysis, statistics, machine learning, deep learning, computer vision, NLP, as well as current deployment and MLOps practices to prepare learners for data-science careers in 2025. The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. ... -
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TechCPP
C++ learning and interview guide aimed at back-end systems developers
TechCPP is a comprehensive C++ learning and interview guide aimed at back-end and systems developers preparing for professional roles. It gathers frequently asked concepts and deep dives—value categories (lvalue/rvalue), perfect forwarding, casts, memory models, atomics, and more—into a structured, readable format. The material goes beyond syntax to discuss performance, optimization techniques, and how standard library containers are implemented under the hood. -
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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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English-level-up-tips
An advanced guide to learn English which might benefit you a lot
English-level-up-tips is a comprehensive open-source guide designed to help learners improve their English language skills across a broad range of competencies, from vocabulary and grammar to listening, speaking, reading, and writing. Structured as a language learning tutorial, the project aggregates tips, strategies, explanations, and resources that go beyond simple phrase lists, encouraging learners to develop a deep understanding of how English works and how to use it effectively. The repository includes structured sections that address different skill areas with lessons, exercises, and recommended approaches tailored to learners at various stages of proficiency. ... -
Get your free 3CX license delivered to your inbox. Easy deployment and management; on premise or in the cloud, 3CX includes features such as: mobile apps, web conferencing, live chat, click2call and more, for UNLIMITED users.3CX is a software-based, open standards IP PBX that offers complete Unified Communications, out of the box. Suitable for any business size or industry 3CX can accommodate your every need; from mobility and status to advanced contact center features and more, at a fraction of the cost. 3CX makes installation, management and maintenance of your PBX so easy that you can effortlessly manage it yourself, whether on an appliance or server at your premise on Windows, Linux or in the cloud.
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10
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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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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Rust Course
It has been the world's most popular language for 8 consecutive years
...The course is carefully designed with a structured catalog, vivid and approachable language, and an engaging style that avoids the dry and mechanical tone of many technical books. It covers the basics of Rust, such as ownership, borrowing, lifetimes, traits, and generics, but also dives deep into advanced topics like performance optimization, linked list implementations, async programming with Tokio, standard library internals, Cargo usage, and WebAssembly development. The project emphasizes practical learning through exercises, helping users approach Rust study as if it were a university course. It also provides a "Cookbook" section of practical code snippets for common tasks such as file operations, regex handling, and database interactions, allowing learners to quickly reference solutions without searching externally. -
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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. ... -
14
Aisthully Desktop Suite
The Freedom to Create. The Power to Master.
Aisthully™ Suite The Freedom to Create. The Power to Master. A complete, downloadable learning ecosystem for Windows. Aisthully combines a professional-grade quiz creation tool (Quiz Studio) with a powerful, analytics-driven study application (Quiz Player). Stop using rigid, one-size-fits-all tools. Start building and mastering your curriculum. The "Quiz Studio" An expert workshop for anyone who teaches. This is where you build your lessons, quizzes, and exams with unparalleled... -
15
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. ... -
16
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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Hello AI World
Guide to deploying deep-learning inference networks
...Ready to dive into deep learning? It only takes two days. We’ll provide you with all the tools you need, including easy to follow guides, software samples such as TensorRT code, and even pre-trained network models including ImageNet and DetectNet examples. Follow these directions to integrate deep learning into your platform of choice and quickly develop a proof-of-concept design. -
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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... -
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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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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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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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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
...The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. Using deep learning models like CNN and RNN with financial and alternative data, and how to generate synthetic data with Generative Adversarial Networks, as well as training a trading agent using deep reinforcement learning.
