Search Results for "deep" - Page 5
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Showing 118 open source projects for "deep"
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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, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc. -
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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,... -
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pytorch-examples
Simple examples to introduce PyTorch
...By emphasizing readable code, the repository helps users understand how PyTorch’s imperative programming style enables flexible model development. It also serves as a quick reference for common patterns and techniques used in deep learning workflows. The project aligns with PyTorch’s philosophy of combining usability with performance and flexibility. Overall, pytorch-examples is an essential learning resource for anyone working with PyTorch. -
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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. -
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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. ... -
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SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with modifications tailored for face detection tasks. ... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more robust to noise and adversarial examples. This repository implements mixup for the CIFAR-10 dataset, showcasing its effectiveness in improving generalization, stability, and calibration of neural networks. ... -
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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. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
...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. -
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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. ... -
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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. -
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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'... -
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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. ... -
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cnn-benchmarks
Benchmarks for popular CNN models
...Overall, cnn-benchmarks is a practical tool for performance analysis in deep learning workflows. -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
...The explanations are present in the wiki associated with this repository. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. ... -
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PrettyTensor
Pretty Tensor: Fluent Networks in TensorFlow
Pretty Tensor is a high-level API built on top of TensorFlow that simplifies the process of creating and managing deep learning models. It wraps TensorFlow tensors in a chainable object syntax, allowing developers to build multi-layer neural networks with concise and readable code. Pretty Tensor preserves full compatibility with TensorFlow’s core functionality while providing syntactic sugar for defining complex architectures such as convolutional and recurrent networks. -
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Python configuration parser module which supports arbitrarily deep hierarchical option "dictionaries" and partnership with the existing optparse (optik) python module. The API of this module will follow as close as possible the optparse model.
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Cover Your Asp, a Python coverage analysis tool. This coverage tool correctly traces multithreaded applications and uses deep understanding of Python syntax to provide intelligent reports.
