Search Results for "deep learning" - Page 19
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Showing 659 open source projects for "deep learning"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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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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MTBook
Machine Translation: Foundations and Models
This is a tutorial, the purpose is to introduce the basic knowledge and modeling methods of machine translation systematically, and on this basis, discuss some cutting-edge technologies of machine translation (formerly known as "Machine Translation: Statistical Modeling and Deep Learning") method"). Its content is compiled into a book, which can be used for the study of senior undergraduates and graduate students in computer and artificial intelligence related majors, and can also be used as reference material for researchers related to natural language processing, especially machine translation. This book is written in tex, and all source codes are open. ... -
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Tez
Tez is a super-simple and lightweight Trainer for PyTorch
Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch. tez (तेज़ / تیز) means sharp, fast & active. This is a simple, to-the-point, library to make your PyTorch training easy. This library is in early-stage currently! So, there might be breaking changes. Currently, tez supports cpu, single gpu and multi-gpu & tpu training. More coming soon! Using tez is super-easy. ... -
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DouZero
[ICML 2021] DouZero: Mastering DouDizhu
DouZero is a reinforcement learning-based AI for playing DouDizhu, a popular Chinese card game. It focuses on perfecting AI strategies for competitive play using value-based deep RL techniques. -
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Sklearn TensorFlow
Sklearn and TensorFlow: A Practical Guide to Machine Learning
...It focuses on teaching core machine learning concepts using Python while demonstrating practical workflows with popular libraries like Scikit-Learn and TensorFlow. The material covers topics ranging from basic machine learning theory to deep learning techniques and model evaluation, enabling learners to build and experiment with models step by step. -
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PyTorchVideo
A deep learning library for video understanding research
PyTorchVideo is a deep learning library for video understanding, providing modular components and pretrained models for tasks like action recognition, video classification, detection, and self-supervised learning. It is tightly integrated with PyTorch and PyTorch Lightning, offering flexible APIs for building and training spatiotemporal networks. The library includes efficient implementations of state-of-the-art architectures such as SlowFast, X3D, and MViT, optimized for both research prototyping and production inference. ... -
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Mocking Bird
Clone a voice in 5 seconds to generate arbitrary speech in real-time
MockingBird is an open-source voice cloning and real-time speech generation toolkit that lets you clone a speaker’s voice from a short audio sample (reportedly as little as 5 seconds) and then synthesize arbitrary speech in that voice. It builds on deep-learning based TTS / voice-cloning technology (in the lineage of projects such as Real-Time-Voice-Cloning), but extends it with support for Mandarin Chinese and multiple Chinese speech datasets — broadening its applicability beyond English. The codebase is implemented in Python (with PyTorch) and includes modules for encoder, synthesizer, vocoder, preprocessing, and inference, as well as demo scripts and a web-server interface for easier experimentation or deployment. ... -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
...For finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time. -
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libpython-clj
Python bindings for Clojure
libpython-clj is a deep interop library enabling you to load and use Python modules from within Clojure as if they were native namespaces—and even extend Python objects from Clojure. It bridges to the Python C API, preserving REPL‑based workflows. Bridge between JVM objects and Python objects easily; use Python in your Java and use some Java in your Python. Python objects are linked to the JVM GC such that when they are no longer reachable from the JVM, their references are released.... -
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deep-learning-for-image-processing
deep learning for image processing including classification
deep-learning-for-image-processing is an extensive educational repository covering practical deep learning methods for computer vision. It organizes implementations, explanations, presentation files, and video lessons around major neural network architectures. The material teaches both model structure and training workflows, with examples built in PyTorch and TensorFlow through Keras. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Machine Learning Collection
A resource for learning about Machine learning & Deep Learning
A resource for learning about Machine learning & Deep Learning. In this repository, you will find tutorials and projects related to Machine Learning. I try to make the code as clear as possible, and the goal is be to used as a learning resource and a way to look up problems to solve specific problems. For most, I have also done video explanations on YouTube if you want a walkthrough for the code. -
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SimSiam
PyTorch implementation of SimSiam
SimSiam is a PyTorch implementation of “Exploring Simple Siamese Representation Learning” by Xinlei Chen and Kaiming He. The project introduces a minimalist approach to self-supervised learning that avoids negative pairs, momentum encoders, or large memory banks—key complexities of prior contrastive methods. SimSiam learns image representations by maximizing similarity between two augmented views of the same image through a Siamese neural network with a stop-gradient operation, preventing... -
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nlp-tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
nlp-tutorial is an educational repository that implements influential natural language processing models in concise PyTorch examples. Most implementations use fewer than 100 lines of executable code, making the architectures easier to inspect and modify. The curriculum begins with neural language models, Word2Vec, and FastText. It then covers TextCNN, recurrent networks, LSTM, bidirectional LSTM, sequence-to-sequence learning, and attention. Later examples implement the Transformer and BERT... -
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Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications. -
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Old Photo Restoration
Bringing Old Photo Back to Life (CVPR 2020 oral)
We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize. Therefore, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. ... -
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neurojs
A JavaScript deep learning and reinforcement learning library
neurojs is a JavaScript framework designed to enable deep learning and reinforcement learning directly within web environments. The library provides a full machine learning framework implemented in JavaScript that can run inside browsers or Node.js environments. It focuses particularly on reinforcement learning algorithms, enabling developers to create intelligent agents that learn through interaction with simulated environments. -
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course-v3
The 3rd edition of course.fast.ai
course-v3 repository contains the complete learning materials for the third edition of the Practical Deep Learning for Coders course developed by the fast.ai research group. The repository includes Jupyter notebooks, lesson materials, datasets, and supporting documentation used in the course to teach modern deep learning techniques. The course emphasizes a top-down approach to learning artificial intelligence, where students begin by building practical models and later study the underlying theory and mathematics. ... -
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Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
...Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data. -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
Torch Points 3D is a framework for developing and testing common deep learning models to solve tasks related to unstructured 3D spatial data i.e. Point Clouds. The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! -
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YOLOv4-large
Scaled-YOLOv4: Scaling Cross Stage Partial Network
YOLOv4-large is an open-source implementation of the Scaled-YOLOv4 object detection architecture, designed to improve both the accuracy and scalability of real-time computer vision models. The project provides a PyTorch implementation of the Scaled-YOLOv4 framework, which extends the original YOLOv4 architecture using Cross Stage Partial (CSP) networks and new scaling techniques. Unlike earlier object detection systems that only scale depth or width, this architecture scales multiple aspects... -
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend.... -
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TNN
Uniform deep learning inference framework for mobile
TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's... -
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Machine Learning Beginner
Machine Learning Beginner Public Account Works
Machine Learning Beginner targets newcomers who are just getting started with machine learning and need a gentle, guided path. It introduces the core vocabulary and the mental map of supervised and unsupervised learning before moving into simple algorithms. The materials prioritize conceptual clarity, then progressively add code to solidify understanding. Step-by-step examples help learners see how data preparation, model training, evaluation, and iteration fit together. Because the scope is... -
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SRU
Training RNNs as Fast as CNNs
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU... -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario... -
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DeepSpeech
Open source embedded speech-to-text engine
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the instructions in the usage docs. If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page.
