#Deep Learning

Explore Trax, the deep learning library prioritizing code clarity and speed. Maintained by Google Brain, it features pre-trained models like Transformers and welcomes community contributions. Trax supports diverse environments from Python scripts to shell, operates on CPUs, GPUs, and TPUs, and integrates TensorFlow Datasets for data handling. It simplifies model training with functional pipelines, providing accessible high-performance deep learning solutions.

Comprehensive TensorFlow tutorials for deep learning beginners, covering from linear models to advanced concepts. Updated for TensorFlow 2, includes YouTube videos and Google Colab support for easy access, serving both theoretical and practical needs.

Access essential cheat sheets offering insights and quick references for engineers in machine learning and deep learning. These resources cover tools like TensorFlow, Keras, PyTorch, and Numpy, and are tailored to support both novices and experienced professionals in improving their skills and efficiency. Each cheat sheet is crafted to present clear and informative content, enabling swift comprehension of key methodologies. Explore a variety of frameworks and libraries that facilitate modern AI and data science applications.

This repository provides a high-quality PDF version of the 'Deep Learning' book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published by MIT Press. Designed for students and professionals interested in machine learning, it offers chapter-wise and complete PDFs as an accessible alternative to the official HTML format. Complementary materials like exercises and lecture slides further enrich this resource. Supporting the authors via purchasing the book on Amazon is recommended.

LayoutParser provides a cohesive toolkit for Document Image Analysis, featuring deep learning models and APIs for layout detection, OCR, and data visualization. It accommodates formats such as JSON, CSV, and PDFs and facilitates model and pipeline sharing within its community. With easy installation and modular features, it boosts processing efficiency and accuracy, making it suitable for developers working with complex document structures. Known for its open community platform and thorough documentation, LayoutParser meets the needs of those interested in document management and deep learning.

This guide delves into the core concepts and practical uses of deep learning with PyTorch, targeted towards developers. It covers essential theories and hands-on coding examples, though it omits topics like recurrent neural networks to center on helping readers navigate advanced subjects independently. Perfect for those versed in Python, it lays a robust foundation for developing deep learning skills.

Explore a detailed guide to PyTorch with tutorials, video lessons, and suggested readings. Discover practical applications in NLP and computer vision using a variety of PyTorch repositories. This resource caters to learners of all levels, providing comprehensive support from foundational neural network concepts to advanced model training techniques.

Discover the third edition of Practical Deep Learning for Coders with fastai1 notebooks in the 'nbs' folder. Note that these notebooks aren't compatible with the latest fastai version; for an updated version using the newest fastai, another repository is available. This guide is ideal for deep learning enthusiasts looking to elevate their coding skills with a practical and fastai1-focused approach.

The project offers an in-depth look into AI concepts ranging from basic to advanced levels, covering areas like machine learning, deep learning, and BERT-based natural language processing. It includes extensive tutorials and datasets, making it suitable for learners at different stages. The curriculum spans key areas such as foundational mathematics, machine learning competitions, the basics of deep learning, and a user-friendly Transformer guide. The materials are regularly refreshed to reflect the latest in AI development, providing a clear and thorough understanding of AI models.

Explore a vast collection of over 500 projects in AI, machine learning, deep learning, computer vision, and natural language processing, all complete with source code. Continuously updated and tested, each project offers valuable insights into practical implementations and the latest advancements. Ideal for professionals aiming to enhance skills or engage in open-source contributions, this repository covers diverse domains like Python projects, healthcare, data analysis, and more. Discover projects ranging from basic to advanced levels, providing substantial learning opportunities and hands-on experience with real-world applicability. Contributions from numerous developers ensure a rich and expansive resource perfect for those interested in innovative AI development.

PyTorch provides tensor computation with GPU support and dynamic neural networks using an autograd system. It integrates with Python, allowing use of libraries like NumPy and SciPy for flexible scientific computations. Features include a dynamic network structure, memory-efficient usage, and simple extensibility. Suitable for researchers and developers exploring AI and machine learning.

The project provides a well-organized collection of tutorials, articles, and resources relevant to Machine Learning and Deep Learning. It serves as a hub for educational content across various topics, including Artificial Intelligence, Natural Language Processing, and Statistical Learning. Contributors are encouraged to help expand this list by following the guidelines. The repository also offers targeted resources in languages like R and Python, enhancing skills in Data Science, NLP, and Machine Learning. This structured content spans basic introductions to advanced topics like Reinforcement Learning and Bayesian Machine Learning, offering resources suitable for both newcomers and experienced users.

This project features a bottom-up approach to real-time multi-person pose estimation, removing the need for person detectors. It achieved recognition in the 2016 MSCOCO Keypoints Challenge and the ECCV Best Demo Award. The approach is implemented across various platforms including C++, TensorFlow, and PyTorch, providing flexible options for developers. The Python code aligns with the latest MSCOCO models and suits diverse system inputs from images to webcams, leveraging deep learning for enhanced human pose recognition.

Explore a tutorial focused on applying deep learning in image processing, without overstatements or promotional language. The course targets learners at all levels, offering video sessions on constructing and training networks using PyTorch and TensorFlow. Gain insights into models like LeNet, AlexNet, ResNet, and their application across tasks such as classification, detection, and segmentation. Detailed navigation includes network explanations and coding examples, with resources like downloadable PPTs for an efficient learning path.

This repository provides a full PyTorch adaptation of the 'Dive Into Deep Learning' book, shifting from the original MXNet base. It spans key concepts in deep learning including data manipulation, linear regression, attention mechanisms, and computer vision, across various detailed chapters. The project welcomes contributions for continuous enhancement, serving as a valuable resource for developers aiming to advance their PyTorch deep learning skills. It is recommended to clone the repository or employ nbviewer for optimal notebook viewing.

This repository provides detailed PyTorch deep learning tutorials aligned with the Udacity Deep Learning Nanodegree, featuring projects such as bike-sharing predictions and dog breed classification. It also includes topics like CNNs, RNNs, GANs, as well as weight initialization and batch normalization. Additionally, learn to deploy models with AWS SageMaker for a comprehensive learning experience.

Explore a rich collection of AI resources spanning tools, courses, and literature, aimed at providing professionals and enthusiasts with a deep dive into key AI fields like machine learning and natural language processing. The guide offers segmentations into chat, image, and video creation tools, supported by educational material from renowned institutions to facilitate comprehensive understanding and application of AI innovations. Discover ongoing insights through curated journals and blogs, further enriched by free access to a variety of content.

This open-source course provides a thorough examination of artificial intelligence and deep learning systems, based on comprehensive industry insights. Key topics include AI hardware, coding, inference systems, and essential framework technologies. Targeted at advanced undergraduate and graduate students as well as AI professionals, it offers understanding of AI's complete stack and lifecycle. The course includes text materials, video content on major platforms, and open-access presentations on GitHub, supporting community-based AI exploration and study.

Delve into an extensive collection of transfer learning resources, featuring academic papers, tutorials, and code implementations. Stay informed about cutting-edge developments and foundational theories in domain adaptation, deep transfer learning, and multi-task learning. Access various educational materials, including video tutorials, profiles of eminent scholars, and prominent research papers, offering essential insights for both newcomers and experienced researchers.

This extensive repository of AI courses provides free educational materials for learners at all levels, including topics such as Generative AI, Natural Language Processing, and Deep Learning. Featuring resources from renowned institutions like MIT, Stanford, and Harvard, it serves as a valuable tool for anyone looking to deepen their understanding of artificial intelligence. Contributions are welcome to continually expand this growing collection.

This open-source resource is designed for beginners interested in leveraging PyTorch for deep learning projects and research. Regularly updated, it aligns with the latest PyTorch releases, providing timely and practical insights for users. Covering topics from basic tensors and autograd to advanced concepts like CNN, RNN, and multi-GPU training, this handbook spans a wide gamut of learning materials. It includes online guides, practical exercises, and community support to aid real-world application.

The MIT Introduction to Deep Learning offers a series of self-paced labs utilizing Google's Colaboratory for a cloud-based learning experience. Access essential resources such as Python notebooks and the 'mitdeeplearning' package, which are fundamental for completing lab exercises. Lecture slides and videos are openly available online, and the materials are distributed under the MIT License, promoting a thorough understanding of deep learning principles. Delve into these resources to advance skills in contemporary data science fields.

Discover detailed TensorFlow code examples from Stanford's CS 20 course, specifically designed for deep learning research. This regularly updated repository includes the syllabus, lecture notes, and content from past courses to enhance comprehension of deep learning methodologies. Built for Python 3.6 and TensorFlow 1.4.1, it offers setup guidance and necessary dependencies, serving as a valuable tool for both novice and seasoned AI learners.

The TensorFlow template aids in structuring deep learning projects with a focus on simplicity, optimized folder organization, and sound OOP principles. It efficiently manages shared tasks and shifts focus to primary components such as models and training routines. Noteworthy features include a user-friendly architecture, a detailed folder structure with model and trainer templates, and Comet.ml integration for real-time metrics and version control. The open-source project invites community contributions and feedback for ongoing improvements.

Explore PyTorch's take on 'Dive into Deep Learning', transitioning from MXNet. This resource hosts detailed Jupyter notebooks and markdown documentation, tailored for beginners eager to explore deep learning with PyTorch. Providing various resource access methods, the project encourages user contributions without requiring deep learning expertise. Study advanced topics like CNNs, RNNs, and optimization algorithms through hands-on exercises.

Explore the MIT Deep Learning repository, which features a well-rounded set of tutorials focused on neural network basics, driving scene segmentation, and advanced techniques like generative adversarial networks. The DeepTraffic competition further enriches your learning experience by offering practical challenges in deep reinforcement learning. This evolving resource, aligned with MIT's ongoing courses, serves as a beneficial tool for newcomers and experienced practitioners in artificial intelligence.

This repository offers tools and examples for developing NLP systems using cutting-edge AI techniques. It features Jupyter notebooks and utility functions for state-of-the-art scenarios, supporting multilingual tasks such as text classification and intelligent chatbots. This resource highlights the use of pretrained models like BERT and transformers to speed up solution development, including integration with Azure Machine Learning and the use of prebuilt APIs for effective NLP task management.

Explore TensorLayer, a versatile deep learning library crafted for both researchers and engineers. It emphasizes flexibility, simplicity, and performance, and is compatible with frameworks like TensorFlow and PyTorch. TensorLayer offers an extensive array of neural layers, comprehensive tutorials, and applications powered by a vibrant community acknowledged by the ACM Multimedia Society. It facilitates swift development of complex AI models, supports diverse hardware, and offers both high-level and professional APIs, with rich multilingual documentation and numerous examples available.

This comprehensive repository includes 35 Python notebooks focusing on data manipulation, classical machine learning, computer vision, and NLP. It offers practical guidance on MLOps, TensorFlow, and Scikit-Learn, enhanced by hands-on exercises. Topics encompass deep learning architectures, data analysis, visualization, and model deployment. Regular updates include transfer learning methods and advanced neural network techniques, beneficial for data scientists and machine learning engineers.

Discover extensive deep learning applications and inference deployment frameworks in this updated resource. This tutorial builds upon the first edition, offering foundational concepts and guiding from basic knowledge to industry applications in computer vision, NLP, and large language models. It details PyTorch fundamentals and projects covering image processing, text generation, and model deployment with ONNX and TensorRT, allowing learners to apply theory in practice. Designed for AI learners, students, and professionals aiming to extend their understanding and practical skills in PyTorch.

The project provides Python-based implementations of key machine learning models, focusing on transparent explanations over optimization. Explore examples like Polynomial Regression and CNN Classification. It includes supervised, unsupervised, reinforcement, and deep learning approaches, offering a thorough guide for foundational machine learning exploration.

Published by Packt, this repository houses comprehensive Python examples from the 'Python Machine Learning, 2nd Edition' book. It encompasses crucial machine learning topics including classification, data processing, ensemble learning, and deep learning with TensorFlow. Tailored for both beginners and experienced practitioners, each chapter delivers practical guidance enriched with theoretical understanding. This edition enhances graphical elements, resolves previous inaccuracies, and introduces new sections on managing imbalanced data and deep learning methods.

This repository offers a curated selection of resources and study notes on AI, ML, and DL, aimed at engineers, developers, and data scientists. It provides easy access to key materials in areas like natural language processing and neural networks. It supports community contributions and regular updates enhance engagement. Explore practical guides, tools, and libraries designed to expand understanding of AI and ML.

Access concise, language-diverse deep learning resources via comprehensive cheatsheets from Stanford's CS 230 course, detailing crucial neural network concepts and model training strategies. Covering convolutional and recurrent networks, these resources provide essential training tips for global accessibility via a dedicated online platform.

Discover the extensive code repository of 'Python Machine Learning, 3rd Ed.', a crucial resource for understanding machine learning with Python and TensorFlow. This edition encompasses topics like classification, regression, clustering, neural networks, and reinforcement learning. It offers practical advice on data pre-processing, model evaluation, and hyperparameter optimization, featuring hands-on examples for real-world applications like sentiment analysis and image classification.

The LeeDL Tutorial, based on the acclaimed 2021 course from Professor Hung-yi Lee of Taiwan University, provides an engaging introduction to deep learning. By using anime-themed examples and a humorous teaching style, it breaks down complex theories. This tutorial offers detailed formula derivations, focusing on challenging concepts and expanding beyond earlier lectures to broaden understanding. Ideal for those interested in Chinese language explanations, it aids in developing deep learning intuition and exploring specialized areas.

Muzic, developed by researchers at Microsoft Research Asia, explores AI in music understanding and generation via deep learning. It includes innovations like MusicBERT for symbolic music, PDAugment for lyrics transcription, and CLaMP for pre-training. Music creation technologies such as SongMASS and PopMAG are included for songwriting and accompaniment, respectively. MuseCoco and HiFiSinger are featured for text-to-music translation and singing synthesis, with MusicAgent using large language models for enhanced music processing.

Deepo is an open-source project designed to simplify the creation and management of deep learning environments. It allows customization of Docker images through modular assembly and automatic dependency resolution, compatible with CUDA, cuDNN, TensorFlow, PyTorch, among others. While no longer maintained, Deepo supports GPU acceleration and offers pre-built images inclusive of Linux, Windows, and OS X platforms, making it a beneficial resource for reducing configuration complexities in deep learning workflows.

Access comprehensive lecture notes and resources from leading courses like Andrew Ng's Machine Learning and MIT's Deep Learning. Ideal for AI enthusiasts seeking insights into advanced and foundational AI topics.

This repository provides PyTorch implementations for both CycleGAN and pix2pix, supporting both unpaired and paired image-to-image translation. Ideal for research and experimentation, the codebase is compatible with the latest PyTorch releases and optimizes training with advanced models like img2img-turbo and contrastive-unpaired-translation. It includes resources such as training tips, FAQs, Docker support, and Colab notebooks for practical usage.

Lightning-Bolts extends PyTorch Lightning with additional components such as callbacks and datasets, facilitating applied research and production. It includes features like the Torch ORT Callback for enhanced training and inference speeds on NVIDIA or AMD GPUs. The SparseMLCallback further introduces sparsity in model fine-tuning using the DeepSparse engine. Encouraging community contributions, Lightning-Bolts evolves to support diverse machine learning needs in different domains without exaggeration.

Discover in-depth notes and summaries from DeepLearning.ai courses, focused on key topics like neural networks, optimization, and machine learning applications. Access expert reviews and insights, perfect for advancing skills in AI across different sectors.

This GitHub repository is a rich source of machine learning examples and tutorials aimed at boosting learning efficiency. The materials are neatly organized by course folders, connecting directly to educational content. Some newer examples utilize Google Colab, but the repository provides essential groundwork in areas like Natural Language Processing, Time Series Analysis, and Financial Engineering. These resources complement courses from deeplearningcourses.com, and offer practical insights into deep learning and AI. Cloning the repository is advised to stay updated with the latest content.

Explore a curated selection of over 30 deep learning and machine learning project ideas suitable for academic and industry contexts. These projects cover skill levels from beginner to advanced research, featuring domains like natural language processing, time series forecasting, and recommendation systems. Discover innovative approaches in image and video processing, music and audio analysis. Engage in hackathon opportunities and explore advanced topics such as semantic search and knowledge base QA. A valuable resource for students, researchers, and developers seeking to broaden their understanding of AI and machine learning.

Caffe is a versatile deep learning framework from Berkeley AI Research (BAIR) and BVLC. It prioritizes speed and modularity, offering extensive documentation, model resources, and installation guides. Custom distributions like Intel Caffe for CPU and OpenCL Caffe for various devices ensure flexibility. The project encourages community engagement via forums and gitter chat, supporting discussions on models and methods. Licensed under BSD 2-Clause, Caffe is suited for research and commercial use.

This project offers a fresh perspective on deep learning concepts through detailed mathematical derivations and fundamental principles, with code implementations using Python and NumPy. It delves into subjects such as deep feedforward networks, convolutional networks, and sequence modeling, providing practical insights and applications in natural language processing and computer vision. The work, complete with annotated code, aids students and practitioners without the dependency on existing frameworks. Frequent updates maintain exhaustive coverage and transparency for all audiences.

This repository provides concise tutorial codes for deep learning researchers to learn PyTorch efficiently, with models mostly under 30 lines of code. It presents a coherent learning trajectory from foundational topics like PyTorch basics, linear regression, to advanced subjects such as Generative Adversarial Networks and Neural Style Transfer. This guide serves as a practical supplement to the Official PyTorch Tutorial, offering vital resources for developing proficiency in PyTorch. The prerequisites include Python 2.7 or 3.5+ and PyTorch 0.4.0+.

The project provides an accurate method for detecting 2D and 3D facial landmarks through Python, utilizing FAN's deep learning techniques. It is compatible with several face detectors such as SFD, Dlib, and BlazeFace and can handle batch processing for directories. Operating efficiently on both CPU and GPU, it is optimized for devices with CUDA capabilities. Users can select different precision settings to improve performance. The installation is simple via pip or conda, with options for source builds and Docker support. User contributions and feedback are welcomed to enhance the project.

DenseNet is a network architecture that efficiently connects layers within dense blocks, optimizing learning and reducing parameters. It excels in accuracy on CIFAR10/100 and ImageNet using fewer resources. Versatile implementations in PyTorch and TensorFlow, coupled with memory-efficient features, make it ideal for various applications. Comprehensive documentation and pre-trained models are available for developers seeking adaptable solutions in image classification.

Explore advanced image enhancement with Keras implementations of Residual Dense Networks for single image super-resolution. This Python-based project enhances image resolution efficiently, utilizing content and adversarial losses. It includes support for Docker and Google Colab, making it ideal for cloud-based applications. Freely available under Apache 2.0 license and compatible with Python 3.6, it welcomes community contributions.