#Neural Networks

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.

The 12-week program offers an introduction to Artificial Intelligence, covering neural networks, deep learning, and practical experience with TensorFlow and PyTorch. It delves into different AI methodologies, including symbolic AI and genetic algorithms, while omitting business uses and deep mathematics. Ethical implications and additional opportunities for learning are available through resources like Microsoft Learn.

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.

This repository provides straightforward tutorials on TensorFlow, featuring well-structured code examples for efficient learning. Updated for TensorFlow 2.3, it offers resources for both beginners and experienced users, covering topics from basic machine learning to advanced training techniques. Each tutorial includes source code and documentation for practical learning. Join a growing community and explore additional resources such as a free TensorFlow Roadmap eBook.

This project offers a structured collection of deep learning resources. It's designed to guide both developers and researchers through complex topics. Resources are categorized and include papers, models, and real-world applications. Additionally, it provides access to a Python machine learning book and a Slack community for continuous learning. As an open-source initiative, it invites global collaboration and contributions.

This content presents a detailed overview of the 'Zero to Mastery Deep Learning with TensorFlow' course, focusing on foundational concepts, neural network construction, and TensorFlow/Keras application. The course features step-by-step coding challenges and project-based learning for enhanced deep learning proficiency. Key components include comprehensive course materials, regular updates, practical exercises, video tutorials, an online book, and a TensorFlow Cheatsheet, catering to users with basic Python and machine learning background.

This repository provides step-by-step tutorials on implementing sequence-to-sequence models with PyTorch for translating German to English text. It covers Python 3.9 dependency installation, spaCy tokenization, and seq2seq model workflows. The tutorials enhance translation outcomes by exploring encoder-decoder models from LSTMs to attention mechanisms, ideal for developers and researchers interested in neural network-based statistical machine translation.

Explore an Elixir toolset powered by Nx for neural network development featuring decoupled APIs for functional definitions, model creation, and training inspired by PyTorch Ignite. With Polaris for optimization and ONNX integration, it offers extensibility and efficiency without dependency constraints.

Attorch is a Python library derived from PyTorch, utilizing Triton for optimized neural network modules. Developed for easy customization, it includes layers applicable to various fields beyond NLP, like computer vision, supporting both training and inference processes. Attorch provides an efficient pathway to develop custom deep learning operations while using familiar PyTorch functionalities. With minimal dependencies, it blends effortlessly with current PyTorch setups, offering fallback options and robust testing capabilities. It is well-suited for developers looking to enhance performance without deep CUDA knowledge.

This resource offers a curated collection of significant deep learning papers focused on enhancing search, recommendation, and advertising in industrial contexts. It covers key research areas like embedding, matching algorithms, and ranking methods, providing insights into the latest machine learning strategies. This compilation is essential for professionals and researchers aiming to leverage deep learning for improved digital experiences.

Explore a Python library designed for efficient image segmentation with PyTorch. Create models quickly using its high-level API, choose from 10 architectures like Unet, and leverage 124 pre-trained encoders with access to over 500 more via the timm library. Supports both binary and multi-class segmentation with optimized preprocessing and popular metrics.

This repository hosts a diverse collection of small-scale projects centered around Deep Learning and Data Science, complete with practical implementations and engaging visualizations. It builds on Medium articles to demystify complex neural network challenges, including overseeing practical applications like visualizing neural networks, understanding overfitting, optimization, and object detection. Users can deepen their insights into convolutional neural networks and explore tools for explicating image classification results.

This guide thoroughly investigates various facets of recommender systems, covering introductory concepts through complex machine learning and deep learning techniques. It includes in-depth discussions on collaborative filtering, matrix factorization, and a range of algorithms like FM, FFM, GBDT+LR, AutoRec, and DeepFM. Additionally, it explores practical implementation strategies, evaluation techniques, and engineering considerations crucial for deployment. This objective overview is an invaluable resource for professionals and researchers focused on advancing their comprehension of the architecture and practical applications of recommender systems.

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.

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 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.

Discover key aspects of large language models including essential mathematics, Python, and neural networks in a structured course. Learn to implement and deploy LLM-based applications with advanced techniques. Access interactive tools such as HuggingChat and ChatGPT for enriched learning experiences and detailed practical notebooks. Benefit from a comprehensive resource collection for mastering LLM construction, fine-tuning, and optimization.

Delve into a carefully curated collection of 100 highly cited deep learning papers from 2012-2016, delivering essential insights across numerous research areas. This guide highlights seminal works to aid deep learning researchers in gaining foundational understanding. The selection excludes new releases to maintain focus on impactful studies. Explore influential papers on neural networks, optimization, and unsupervised models that have shaped advancements in artificial intelligence and machine learning.

DL-Simplified offers open-source neural network projects from beginner to expert levels, providing hands-on experiences to simplify deep learning concepts. With a focus on data-driven techniques and a structured contribution framework, it fosters knowledge sharing and community development in deep learning. Discover how to engage with multiple data representation levels and stay informed about ongoing contributions in this dynamic field.

This repository provides a curated collection of key research papers in speech recognition and synthesis, covering areas like Text-to-Audio, Automatic Speech Recognition (ASR), Speaker Verification, Voice Conversion (VC), and Speech Synthesis (TTS). It also delves into specialized topics including Language Modelling, Confidence Estimates, and Music Modelling. The compilation features foundational works and recent advancements, offering valuable insights for researchers and practitioners in the field of audio processing. This serves as an extensive knowledge base for understanding the evolution of techniques and applications influencing today's speech and audio processing developments.

Discover the latest research with automated insights from ArXiv paper analyses. This resource offers question-answering capabilities across diverse topics such as diffusion ranking and cross-lingual generalization. Supported by Anthropic and the `Claude-2.1` API, this project delivers valuable information for academic and professional endeavors, enabling easy access to categorized papers by year.

This series of tutorials offers a detailed guide on sequence classification for sentiment analysis utilizing PyTorch, covering Neural Bag of Words, Recurrent Neural Networks, Convolutional Neural Networks, and BERT transformers. It begins with foundational models and gradually advances in complexity and precision for movie review sentiment prediction. Instructions for environment setup and essential resources are provided, making it suitable for both newcomers and experienced practitioners of sentiment analysis in Python.

This open-source project leverages neural networks to turn grayscale photos into color images, featuring step-by-step tutorials from basic neural models to complex GAN architectures. With insights into color space conversion, this project also explores efficient image resolutions and pretrained model optimizations, offering developers and researchers a comprehensive resource for mastering AI-driven image colorization.

YOLOv5-Lite delivers a streamlined and optimized version of YOLOv5, focusing on reduced computational requirements and accelerated inference times. Ideal for edge devices, it incorporates ablation experiments that result in decreased memory usage and fewer parameters. Key improvements include channel shuffling and an updated YOLOv5 head, maintaining at least 10 FPS on devices such as Raspberry Pi. By removing the Focus layer and refining model quantization, deployment becomes more accessible. Comparative analyses reveal superior inference speed and model efficiency across multiple platforms, making it an effective choice for resource-constrained environments.

This guide provides a regularly updated overview of machine learning, featuring recommended tools, curated educational resources, and practical applications for learners of all levels. It covers fundamental concepts, best practices, and specific fields such as reinforcement learning and deep learning, focusing on accessible resources and effective mastery without exaggeration.

Discover deep learning with TensorFlow and Keras in this course by Jeff Heaton at Washington University. Learn about neural networks like CNN, LSTM, and GAN, and their applications in computer vision, NLP, and data generation. Understand deep learning efficiency on GPUs and explore Python innovations. This course provides a balance of theory and practical skills, connecting knowledge with real-world projects. No prior Python experience needed. Advance your deep learning skills and adapt to new technology trends.

Discover a diverse set of machine learning notebooks covering applications from neural networks to computer vision. These resources on Codespaces offer clear setup guidance for straightforward learning in machine learning fields, including PyTorch and generative adversarial networks, ideal for expanding knowledge or efficiently prototyping.

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.

Engage in a 100-day program designed to deepen understanding of machine learning algorithms, covering initial topics like data preprocessing and linear regression, progressing to advanced subjects such as SVM and deep learning. Daily lessons include practical exercises, theoretical insights, and resources, aimed at professionals and enthusiasts seeking to enhance their skills in algorithms like decision trees and neural networks.

Discover a diverse array of open-source resources and tutorials aimed at providing a solid foundation in machine learning and deep learning. This guide covers essential mathematical concepts, beginner-friendly paths, and advanced topics in ML and DL, including applications in computer vision, NLP, and reinforcement learning. It also provides practical advice on participating in Kaggle competitions and gaining engineering skills essential for future ML engineers.

Ignite offers a high-level library for streamlined training and evaluation of neural networks in PyTorch, emphasizing code simplicity and flexibility. This library provides a robust engine and event system, extensible metrics, and integrated handlers for training management, artifact saving, and parameter logging. It efficiently supports custom function execution and maintains control over the training process. Ignite is easily installable via pip or conda, and supports nightly releases and Docker.

This Coursera specialization by Andrew Ng offers an in-depth look into deep learning with neural networks, regularization, convolutional networks, and sequence models. It includes practical programming assignments and quizzes, and has been updated for TensorFlow 2. Suitable for those interested in AI applications in computer vision and NLP, it covers advanced topics like Transformer networks and YOLO. Includes structured guidance and practical case studies.

This neural network library integrates Python and C++ for use with hardware such as the e150 and n150. It supports a range of models, including LLMs, CNNs, and NLPs, providing enhancements via Tensor and Data Parallelism. See API references and programming guides specific to Tenstorrent's hardware, and review model demos like Falcon7B and Mistral for increased throughput. TT-Metalium is available for kernel development, aided by technical reports and examples. These resources update regularly, catering to ongoing advancements in deep learning.

Discover a Python library tailored for text-to-speech synthesis with a primary focus on Japanese. While the main application is for Japanese, neural network features may also be used for other languages. Easily install using 'pip install ttslearn'. The package includes Jupyter notebooks for learning and advanced TTS recipes using JSUT and JVS corpora. Licensed under MIT, it's suitable for both commercial and non-commercial use. Access detailed documentation for further insights.

The course provides an organized learning path into neural networks, starting from basics to advanced language models without excessive promotion. Offering practical YouTube lectures and hands-on Jupyter notebooks, it guides users through neural networks, backpropagation, and the evolution to complex Transformer and GPT models. The focus is on gaining practical experience in training, optimization, and understanding tokenization in AI, ensuring unbiased, detailed skill development.

Uncertainty calibration techniques enhance prediction reliability in domains like computational advertising and medical diagnosis. This collection reviews parametric, non-parametric, and hybrid approaches, featuring methods such as Platt scaling and innovative models like Mix-n-Match, summarizing advancements in calibration metrics and practical applications. This resource supports research with datasets and evaluation metrics from leading tech companies and academic contributions.

Explore the progressive field of AI through this detailed resource featuring DNN, GAN, NLP, and Big Data. Aimed at deep learning application engineers, it includes information on AI domains such as image classification advancements from LeNet to ResNet and Inception models. This project supports learning through articles, code, and visuals, facilitating understanding of cutting-edge technology and engineering project nuances. Developed by experienced programmers, it provides valuable resources for keeping up with and applying modern AI developments.

Discover the capabilities of Large Language Models in this curated guide perfect for developers and researchers. Gain insights into core mathematics, Python, and neural networks, alongside practices to build intelligent applications. This resource covers LLM architectures, pretraining, fine-tuning, and includes articles on NLP and model evaluation, offering resources for refining skills and advancing LLM projects.

Discover a set of TensorFlow tutorials created specifically for beginners with clear examples for both TF v1 and v2. Learn traditional and modern practices, such as layers, estimators, and dataset APIs. The project includes instructions on basic operations, linear and logistic regressions, word embedding models, gradient boosting, and various neural network architectures. Additional guides cover data management, multi-GPU training, and customized layers. Keep up-to-date with the latest TensorFlow methods and enhance your skills with practical, hands-on experiences.

Explore a structured 100-day journey into machine learning, covering both supervised and unsupervised techniques. This project involves practical coding exercises in algorithms such as linear regression, k-NN, and support vector machines (SVMs). With resources from Coursera and YouTube, enhance your understanding of key concepts like decision trees and neural networks, fostering a comprehensive grasp of machine learning.

Gain a well-rounded understanding of deep learning across five structured courses, featuring practical assignments, quiz solutions, and guidance from Andrew Ng on Coursera. Access a repository rich in resources for mastering neural networks, hyperparameter tuning, and convolutional neural networks, perfect for computer science students and self-learners looking to enhance their machine learning skills and debugging strategies. Build a solid foundation in AI technologies with valuable course content.

This guide assists beginners in exploring machine learning and AI in 2024 with a focus on free resources that do not necessitate any prior programming or math knowledge. It offers a versatile learning approach utilizing online videos, courses, and articles. Optional paid content is available for those seeking deeper insights, but the emphasis remains on self-study and reinforcement. Managed by Louis Bouchard, the guide includes networking and community resources, providing a comprehensive foundation for those eager to learn about machine learning.

Discover deep learning courses covering essentials to advanced topics like NLP and computer vision, curated by experts from top universities like Stanford and MIT.

Discover Neurite, a neural network toolbox for medical image analysis using TensorFlow and Keras. It includes network layers, N-D interpolation utilities, and model stacking strategies. Neurite supports segmentation and analysis with models like UNet and provides generators and metrics for performance evaluation. Easily install by cloning or with pip, and contribute under guided coding standards. Neurite's tools are featured in projects like VoxelMorph and brainstorm.

This repository offers Jupyter Notebooks in alignment with Andrej Karpathy's 'Neural Networks: Zero to Hero' tutorial series. It delivers detailed insights into neural network fundamentals and advanced techniques, including GPT and GPT-2. The materials support learners in expanding their practical understanding of topics such as Micrograd, WaveNet, and GPT tokenization. Contributions to improve the repository are encouraged, fostering collaboration and continuous learning.

Discover the challenges of Sokoban with dynamic room creation, a valuable tool for testing Reinforcement Learning algorithms. This environment aids AI research by varying puzzles to avoid overfitting, offering diverse gameplay options and configurations. Ideal for AI development, it supports multiple modes and adaptations, providing a practical solution for enhancing learning algorithms.

Nagisa offers developers a streamlined Python-based solution for Japanese word segmentation and POS-tagging through the use of recurrent neural networks. This module supports various platforms, such as Linux, macOS, and Windows, and allows for easy dictionary customization and model training. Designed for compatibility with multiple Python versions, Nagisa is versatile and straightforward, making it a valuable asset for natural language processing projects.

A comprehensive collection of Ruby machine learning libraries, data sources, and tutorials tailored for developers at all levels. Curated by The Ruby Science Foundation, this list offers frameworks, neural networks, deep learning, and more, without exaggeration. It fosters knowledge-sharing through project examples, articles, and community engagement without overstating contributions. Explore practical tools and resources to enhance machine learning projects with Ruby.

This seminar, hosted by Harvard's Neurobiology Department, provides graduate students and postdocs an opportunity to explore machine learning models through basic Python sessions. It combines theoretical discussions and practical coding over two evenings, helping participants understand algorithm dynamics and limitations. The program promotes an in-depth grasp of computations, ideal for those in neuroscience or computer science fields.

Delve into a practical and detailed guide on machine learning, focusing on PyTorch and Scikit-Learn's methods and applications. This book shares expert insights on algorithm training, data handling, and model assessment, while venturing into complex areas such as neural networks, deep learning, and reinforcement learning. With code examples and step-by-step instructions, it enhances practical ML knowledge, supported by a comprehensive code notebook repository. Authored by experts Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili, it is a vital resource for anyone looking to expand their understanding of machine learning with PyTorch and Scikit-Learn.