Search Results for "can" - Page 50
Sort By:
Showing 1376 open source projects for "can"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
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.
-
1
Magnitude
A fast, efficient universal vector embedding utility package
A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim but can be used as a generic key-vector store for domains outside NLP. It offers unique features like out-of-vocabulary lookups and streaming of large models over HTTP. Published in our paper at EMNLP 2018 and available on arXiv. -
2
DeepFaceLab
The leading software for creating deepfakes
...DeepFaceLab is an open-source deepfake system that enables users to swap the faces on images and on video. It offers an imperative and easy-to-use pipeline that even those without a comprehensive understanding of the deep learning framework or model implementation can use; and yet also provides a flexible and loose coupling structure for those who want to strengthen their own pipeline with other features without having to write complicated boilerplate code. DeepFaceLab can achieve results with high fidelity that are indiscernible by mainstream forgery detection approaches. Apart from seamlessly swapping faces, it can also de-age faces, replace the entire head, and even manipulate speech (though this will require some skill in video editing). -
3
Douyin-Bot
Python TikTok bot
...It is best understood as a demonstration of app automation, screen capture, API-based image analysis, and scripted mobile interaction. Its main value is showing how Python can orchestrate a phone app workflow by combining ADB, screenshots, recognition logic, and automated gestures. -
4
micrograd
A tiny scalar-valued autograd engine and a neural net library
...On top of the core autograd “Value” concept, the project includes a small neural network library that lets you define and train simple models with a PyTorch-like feel, including multilayer perceptrons. The repository is intentionally compact and readable, prioritizing clarity over performance so learners can follow every step of gradient flow and parameter updates. It is commonly used as a learning bridge between basic calculus intuition and full-scale deep learning frameworks, helping developers understand why autodiff libraries behave the way they do. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
-
5
TensorNets
High level network definitions with pre-trained weights in TensorFlow
...Readability. With recent TensorFlow APIs, more factoring and less indenting can be possible. For example, all the inception variants are implemented as about 500 lines of code in TensorNets while 2000+ lines in official TensorFlow models. Reproducibility. You can always reproduce the original results with simple APIs including feature extractions. -
6
RL-Stock
Automated stock trading through a simulated training environment
...The project includes scripts for collecting stock data, defining a reinforcement learning environment, training an agent, and visualizing results. It focuses on how an agent can learn trading-like behavior through rewards, states, and actions. The repository is useful for learners who want to connect reinforcement learning concepts with a familiar financial market example. Its main value is demonstrating the structure of a deep reinforcement learning trading experiment while making clear that real-world investing requires much more validation and risk control. -
7
TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the theory of transfer learning and show how to apply it in useful projects. The development is on progress! The API will be updated soon, the more talented and light-weight API will be available in this repo! ... -
8
Knock Knock
Get notified when your training ends
...The library solves this problem by adding simple decorators or command-line commands that automatically send notifications when a process completes or crashes. These alerts can be delivered through several communication platforms such as email, Slack, Telegram, or other messaging services. The goal of the project is to allow developers to monitor experiments remotely without needing to stay connected to the training environment. By adding only a few lines of code, the library can wrap around a training function and report execution status. -
9
moosha-ai
An intelligent virtual assistant (IVA) or intelligent personal assista
An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. Sometimes the term "chatbot" is used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. -
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.
-
10
BytePS
A high performance and generic framework for distributed DNN training
BytePS is a high-performance and generally distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on either TCP or RDMA networks. BytePS outperforms existing open-sourced distributed training frameworks by a large margin. For example, on BERT-large training, BytePS can achieve ~90% scaling efficiency with 256 GPUs (see below), which is much higher than Horovod+NCCL. In certain scenarios, BytePS can double the training speed compared with Horovod+NCCL. ... -
11
Aida Lib
Aida is a language agnostic library for text generation
...You can compose operations on your text with some handy operators. -
12
ChainerRL
ChainerRL is a deep reinforcement learning library
...ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used. -
13
textgenrnn
Easily train your own text-generating neural network
With textgenrnn you can easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code. A modern neural network architecture that utilizes new techniques as attention-weighting and skip-embedding to accelerate training and improve model quality. Train on and generate text at either the character-level or word-level. -
14
imgaug
Image augmentation for machine learning experiments
...It supports a wide range of augmentation techniques, allows to easily combine these and to execute them in random order or on multiple CPU cores, has a simple yet powerful stochastic interface and can not only augment images but also key points/landmarks, bounding boxes, heatmaps and segmentation maps. Affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, etc. Rotate image and segmentation map on it by the same value sampled. ... -
15
jieba
Stuttering Chinese word segmentation
...Four word segmentation modes are supported. Precise mode, which tries to cut the sentence most precisely, suitable for text analysis. Full mode, scans all the words that can be formed into words in the sentence, the speed is very fast, but the ambiguity cannot be resolved. The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable for word segmentation in search engines. The paddle mode uses the PaddlePaddle deep learning framework to train the sequence labeling (bidirectional GRU) network model to achieve word segmentation. ... -
16
Bangla TTS
Bangla text to speech synthesis in python
Bangla text to speech Multilingual (Bangla, English) real-time ([almost] in a GPU) speech synthesis library. Installation -------------------------------------- * Install Anaconda * conda create -n new_virtual_env python==3.6.8 * conda activate new_virtual_env * pip install -r requirements.txt * While running for the first time, keep your internet connection on to download the weights of the speech synthesis models (>500 MB) * For... -
17
Snips NLU
Snips Python library to extract meaning from text
...The NLU engine first detects what the intention of the user is (a.k.a. intent), then extracts the parameters (called slots) of the query. The developer can then use this to determine the appropriate action or response. -
18
EverydayWechat
Python tool that automates WeChat messages, replies, & group utilities
...It also supports intelligent automatic replies to incoming messages by integrating with multiple chatbot services. In addition to personal messaging automation, the project includes a group assistant that can respond to queries and provide useful information within chat groups. These group utilities can retrieve data such as weather conditions, calendar details, garbage classification information, movie box office statistics, delivery tracking updates, and air quality reports. -
19
cocoNLP
A Chinese information extraction tool
...The project blends pattern-based methods with NLP heuristics, giving developers dependable results for real-world texts like chats, comments, and user-generated content. Its API is intentionally simple, so you can drop it into scripts, ETL jobs, or dashboards without deep ML expertise. Because it aims at utility over complexity, it’s useful for prototyping data products or building lightweight text analytics where large models would be overkill. The repository also includes examples and test snippets to help you understand expected inputs and typical outputs, which shortens the learning curve for newcomers. -
20
Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
...Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few commands. When training your own model, start with only PSNR loss (50+ epochs, depending on the dataset) and only then introduce GANS and feature loss. This can be controlled by the loss weights argument. The weights used to produce these images are available directly when creating the model object. ISR is compatible with Python 3.6 and is distributed under the Apache 2.0 license. -
21
TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
...Each section focuses on a different aspect of machine learning development, including tensor manipulation, model training, optimization strategies, and data processing techniques. The examples illustrate how TensorFlow operations and tensors can be used to build machine learning pipelines and perform tasks such as regression, classification, and clustering. By combining theoretical explanations with executable code, the project helps developers understand how TensorFlow algorithms operate internally while also providing working examples that can be adapted for real projects. -
22
Machine Learning with TensorFlow
Accompanying source code for Machine Learning with TensorFlow
...The repository includes implementations of algorithms such as logistic regression, convolutional neural networks, and autoencoders, which allow readers to experiment with different learning techniques. Many examples are structured as standalone scripts or notebooks that can be executed directly to reproduce the results described in the book. The code demonstrates how TensorFlow can be used to construct training pipelines, prepare datasets, and evaluate model performance. -
23
RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
24
Jarvis
Personal Assistant for Linux and macOS
Jarvis is a simple personal assistant for Linux, MacOS and Windows which works on the command line. He can talk to you if you enable his voice. He can tell you the weather, he can find restaurants and other places near you. He can do some great stuff for you. In order to start Jarvis just clone this repository and run python installer. Run Jarvis from anywhere by command jarvis. You can start by typing help within the Jarvis command line to check what Jarvis can do for you. ... -
25
gpt2-client
Easy-to-use TensorFlow Wrapper for GPT-2 117M, 345M, 774M, etc.
...Finally, gpt2-client is a wrapper around the original gpt-2 repository that features the same functionality but with more accessiblity, comprehensibility, and utilty. You can play around with all four GPT-2 models in less than five lines of code. Install client via pip. The generation options are highly flexible. You can mix and match based on what kind of text you need generated, be it multiple chunks or one at a time with prompts.