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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
...The library also includes tools for constructing specialized financial data structures, generating predictive features, and evaluating trading strategies through backtesting. Its architecture emphasizes reproducibility, robust testing, and well-documented code so that researchers and practitioners can reliably experiment with financial machine learning models. -
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Tez
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. We don't want you to be far away from pytorch. So, you do everything on your own and just use tez to make a few things simpler. -
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Perceptual Similarity Metric and Dataset
LPIPS metric. pip install lpips
...Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. -
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U-Net Fusion RFI
U-Net for RFI Detection based on @jakeret's implementation
...Sum Threshold (in fusion as an expert, and in testing as a comparison) requires the use of AOFlagger by Andre Offringa. You can find this code at https://gitlab.com/aroffringa/aoflagger. This project will use the aoflagger program within the code, so you may need to ensure that any environment variables are set for aoflagger before use. cite: https://sourceforge.net/p/u-net-fusion-rfi/wiki/cite/ -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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Keepsake
Version control for machine learning
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook. -
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Lambda Networks
Implementation of LambdaNetworks, a new approach to image recognition
...The new method utilizes λ layer, which captures interactions by transforming contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Shinel94 has added a Keras implementation! It won't be officially supported in this repository, so either copy / paste the code under ./lambda_networks/tfkeras.py or make sure to install tensorflow and keras before running the provided commands. -
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Neural Networks Collection
Neural Networks Collection
This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people. -
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captcha_break
Identification codes
...First, we set our verification code format to numbers and capital letters, and generate a string of verification codes. It is well known that tensorflow occupies all video memory by default, which is not conducive to us conducting multiple experiments at the same time, so we can use the following code when tensorflow uses the video memory it needs instead of directly occupying all video memory. -
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SSD
A PyTorch Implementation of Single Shot MultiBox Detector
...It supports commonly used benchmark datasets such as PASCAL VOC and MS COCO, and it also provides scripts to simplify downloading and setting up those datasets. For training visibility, the project includes support for Visdom so users can monitor loss in real time through a browser-based interface. Its structure makes it useful both as a reference implementation for learning SSD and as a base for custom experimentation in detection research or practical computer vision projects. -
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Easy-TensorFlow
Simple and comprehensive tutorials in TensorFlow
The goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format). There is a necessity to address the motivations for this project. TensorFlow is one of the deep learning frameworks available with the largest community. This repository is dedicated to suggesting a simple path to learn TensorFlow. In addition to the... -
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Python Data Science Tutorials
Common data analysis and machine learning tasks using python
...Additional material addresses text mining, sentiment analysis, serialization with pickle, AutoML, regular expressions, and web scraping. The repository is organized by topic so learners can use it as a study roadmap or troubleshooting index. Many links document the earlier Python data science ecosystem, making the project especially valuable as a broad historical resource collection. -
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BossSensor
Hide screen when boss is approaching
...The software relies on libraries such as OpenCV, TensorFlow, and Python-based machine learning tools to perform face detection and classification. Training the system requires a dataset of labeled images representing the boss and other people so that the model can learn to differentiate between them. -
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The GAN Zoo
A list of all named GANs
...Users can browse the dataset or explore a tabular version that allows filtering by year or searching for specific model names. The repository encourages contributions from the community so that newly published GAN architectures can be added to the list. -
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anaGo
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition
anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as named entity recognition (NER), part-of-speech tagging (POS tagging), semantic role labeling (SRL) and so on. Unlike traditional sequence labeling solver, anaGo doesn't need to define any language-dependent features. Thus, we can easily use anaGo for any language. In anaGo, the simplest type of model is the Sequence model. Sequence model includes essential methods like fit, score, analyze and save/load. For more complex features, you should use the anaGo modules such as models, preprocessing and so on. -
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Deep Reinforcement Learning for Keras
Deep Reinforcement Learning for Keras.
...This means that evaluating and playing around with different algorithms is easy. Of course, you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. Documentation is available online. -
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DIGITS
Deep Learning GPU training system
...DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. DIGITS is available as a free download to the members of the NVIDIA Developer Program. DIGITS is available on NVIDIA GPU Cloud (NGC) as an optimized container for on-demand usage. Sign-up for an NGC account and get started with DIGITS in minutes. -
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Intel neon
Intel® Nervana™ reference deep learning framework
...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. The gpu backend is selected by default, so the above command is equivalent to if a compatible GPU resource is found on the system. The Intel Math Kernel Library takes advantages of the parallelization and vectorization capabilities of Intel Xeon and Xeon Phi systems. When hyperthreading is enabled on the system, we recommend the following KMP_AFFINITY setting to make sure parallel threads are 1:1 mapped to the available physical cores. -
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Five video classification methods
Code that accompanies my blog post outlining five video classification
...This reduces model complexity, training time, and a whole whack load of hyperparameters we don’t have to worry about. Every video will be subsampled down to 40 frames. So a 41-frame video and a 500-frame video will both be reduced to 40 frames, with the 500-frame video essentially being fast-forwarded. We won’t do much preprocessing. A common preprocessing step for video classification is subtracting the mean, but we’ll keep the frames pretty raw from start to finish. -
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Scattertext 0.2.1
Beautiful visualizations of how language differs among document types
A tool for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot. Points corresponding to terms are selectively labeled so that they don't overlap with other labels or points. -
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HYBRYD
Library written in C with Python API for IPv6 networking
...I'm trying to readapt it for Python 2.7.3 and GCC 4.6.3 The library has to be build as a simple Python extension using >python setup.py install and allows to create different kind of servers, clients or hybryds (clients-servers) over (TCP/UDP) using the Ipv6 Protocol. The architecture of the code is based on brain architecture. Will put an IPv6 adress active available as soon as possible so that you can download pieces of codes. The aim of that coding was to use primary linux commands easily codable and make an object of an IPv6 connection. Moreover, the model is full-state! -
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pySPACE
Signal Processing and Classification Environment in Python using YAML
pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. ... -
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text_summurization_abstractive_methods
Multiple implementations for abstractive text summurization
This repo is built to collect multiple implementations for abstractive approaches to address text summarization it is built to simply run on google colab , in one notebook so you would only need an internet connection to run these examples without the need to have a powerful machine , so all the code examples would be in a jupyter format , and you don't have to download data to your device as we connect these jupyter notebooks to google drive