Search Results for "scratch"
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Showing 8 open source projects for "scratch"
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Refreshingly simple maintenance management softwareSet up in minutes, not months. Maintainly replaces spreadsheets, clipboards, and broken email chains with a modern CMMS built for how maintenance teams actually work.
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Powerful Website Security | Continuous Web Threat PlatformReflectiz is a comprehensive web exposure management platform that helps organizations proactively identify, monitor, and mitigate security, privacy, and compliance risks across their online environments. Designed to address the growing complexity of modern websites, Reflectiz provides full visibility and control over first, third, and even fourth-party components, such as scripts, trackers, and open-source libraries that often evade traditional security tools.
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Deep Learning Is Nothing
Deep learning concepts in an approachable style
Deep-Learning-Is-Nothing presents deep learning concepts in an approachable, from-scratch style that demystifies the stack behind modern models. It typically begins with linear algebra, calculus, and optimization refreshers before moving to perceptrons, multilayer networks, and gradient-based training. Implementations favor small, readable examples—often NumPy first—to show how forward and backward passes work without depending solely on high-level frameworks. -
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
Hugging Face Transformers provides APIs and tools to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities. Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. Images, for tasks like image classification, object detection, and segmentation. Audio, for tasks like speech recognition and audio classification. ... -
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AudioCraft
Audiocraft is a library for audio processing and generation
...The repo provides inference scripts, checkpoints, and simple Python APIs so you can generate clips from prompts or incorporate the models into applications. It also contains training code and recipes, so researchers can fine-tune on custom data or explore new objectives without building infrastructure from scratch. Example notebooks, CLI tools, and audio utilities help with prompt design, conditioning on reference audio, and post-processing to produce ready-to-share outputs. -
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GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
...We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. For those looking for a TPU-centric codebase, we recommend Mesh Transformer JAX. If you are not looking to train models with billions of parameters from scratch, this is likely the wrong library to use. For generic inference needs, we recommend you use the Hugging Face transformers library instead which supports GPT-NeoX models. -
More Bookings. Better Experience.The all-in-one solution built to help you stay organised and get more bookings with thousands of connections to online travel agencies (OTAs), resellers and suppliers.
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5
Interactive Deep Colorization
Deep learning software for colorizing black and white images
...The project includes both the older Caffe-based implementation and a more recent PyTorch backend, giving flexibility depending on user preference and infrastructure. Because it handles image reading, hint interpretation, and color mapping internally, users don’t need to build the colorization pipeline from scratch: they only need to supply grayscale images (and optionally hints), and the software produces a full-color version. -
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Computer Vision
Best Practices, code samples, and documentation for Computer Vision
...The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in Computer Vision algorithms, neural architectures, and operationalizing such systems. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud. -
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Dive-into-DL-TensorFlow2.0
Dive into Deep Learning
This project changes the MXNet code implementation in the original book "Learning Deep Learning by Hand" to TensorFlow2 implementation. After consulting Mr. Li Mu by the tutor of archersama , the implementation of this project has been agreed by Mr. Li Mu. Original authors: Aston Zhang, Li Mu, Zachary C. Lipton, Alexander J. Smola and other community contributors. There are some differences between the Chinese and English versions of this book . This project mainly focuses on TensorFlow2... -
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DeepLearningProject
An in-depth machine learning tutorial
This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you...
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