Search Results for "deep learning toolbox" - Page 4
Sort By:
Showing 102 open source projects for "deep learning toolbox"
View related business solutions-
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
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.
-
1
Tabnine
Vim client for TabNine
Tabnine is an AI-powered code completion extension trusted by millions of developers around the world. Whether you’re just getting started as a developer or if you’ve been doing it for decades, Tabnine will help you code twice as fast with half the keystrokes – all in your favorite IDE. Whether you call it IntelliSense, intelliCode, autocomplete, AI-assisted code completion, AI-powered code completion, AI copilot, AI code snippets, code suggestion, code prediction, code hinting, or... -
2
...The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. ...
-
3
TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. ... -
4
NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. ... -
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.
-
5
Top Deep Learning Projects
A list of popular github projects related to deep learning
TopDeepLearning is a curated index of the most popular GitHub projects related to deep learning, ranked by their star count. Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly discover reputable and well-maintained repositories. ... -
6
SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
SageMaker MXNet Training Toolkit is an open-source library for using MXNet to train models on Amazon SageMaker. For inference, see SageMaker MXNet Inference Toolkit. For the Dockerfiles used for building SageMaker MXNet Containers, see AWS Deep Learning Containers. For information on running MXNet jobs on Amazon SageMaker, please refer to the SageMaker Python SDK documentation. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. ... -
7
MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. -
8
Consistent Depth
We estimate dense, flicker-free, geometrically consistent depth
Consistent Depth is a research project developed by Facebook Research that presents an algorithm for reconstructing dense and geometrically consistent depth information for all pixels in a monocular video. The system builds upon traditional structure-from-motion (SfM) techniques to provide geometric constraints while integrating a convolutional neural network trained for single-image depth estimation. During inference, the model fine-tunes itself to align with the geometric constraints of a... -
9
Tensor2Tensor
Library of deep learning models and datasets
...Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. T2T was developed by researchers and engineers in the Google Brain team and a community of users. It is now deprecated, we keep it running and welcome bug-fixes, but encourage users to use the successor library Trax. -
Try Google Cloud Risk-Free With $300 in CreditUse your credit across every product. Compute, storage, AI, analytics. When it runs out, 20+ products stay free. You only pay when you choose to.
-
10
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. -
11
PyText
A natural language modeling framework based on PyTorch
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. -
12
ChainerRL
ChainerRL is a deep reinforcement learning library
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. -
13
MADDPG
Code for the MADDPG algorithm from a paper
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. ... -
14
Graph Nets library
Build Graph Nets in Tensorflow
...A graph network takes graphs as inputs, consisting of edges, nodes, and global attributes, and produces updated graphs with modified feature representations at each level. This library implements the foundational ideas from DeepMind’s paper “Relational Inductive Biases, Deep Learning, and Graph Networks”, offering tools to explore relational reasoning and message-passing neural networks. Graph Nets supports both TensorFlow 1 and TensorFlow 2, working with CPU and GPU environments, and includes educational Jupyter demos for shortest path finding, sorting, and physical prediction tasks. The codebase emphasizes modularity, allowing users to easily define their own edge, node, and global update functions. -
15
MLBox
MLBox is a powerful Automated Machine Learning python library
MLBox is a powerful Automated Machine Learning python library. Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the-art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,...) Prediction with model interpretation. -
16
The Google Cloud Developer's Cheat Sheet
Cheat sheet for Google Cloud developers
Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes) by the Google Developer Relations Team. This list only includes products that are publicly available. There are several products in pre-release/private-alpha that will not be included until they go public beta or GA. Many of these products have a free tier. There is also a free trial that will enable you try almost everything. API platforms and ecosystems, developer and management tools,... -
17
abu
Abu quantitative trading system (stocks, options, futures, bitcoin)
Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models,... -
18
captcha_break
Identification codes
This project will use Keras to build a deep convolutional neural network to identify the captcha verification code. It is recommended to use a graphics card to run the project. The following visualization codes are jupyter notebookall done in . If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. It supports image verification... -
19
data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. ... -
20
cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
...The project includes scripts for training, evaluation, and data handling, making it easy to run experiments on datasets such as movie reviews or other labeled text collections. By breaking down the model into understandable components, it serves as a practical reference for students and practitioners learning how deep learning models handle text beyond traditional bag-of-words approaches. -
21
Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
22
Zhao
A compilation of "The Princely Party Relationship Network"
zhao is a repository that consolidates research, data, and insights related to Zhao, which is likely an individual’s research collection, notes, or curated resources on deep learning, AI, or computational topics (name and content context suggest specialized study). The project may include code examples, experiment results, references to academic papers, mathematical notes, and supporting scripts to explore specific ML methods, benchmarks, or theoretical findings. Because it aggregates content associated with Zhao, the repository functions as a personal or shared knowledge base for readers who want insight into a body of research rather than a traditional software library. ... -
23
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. -
24
Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. ... -
25
Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'...
