Stm Libraries

34 Components & Libraries

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Stm Libraries

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

4 September 2023 - Version 1.7.1 25 May 2023 - Version 1.7.0 15 September 2022 - Version 1.6.2 1 September 2022 - Version 1.6.1 24 August 2022 - Version 1.6.0 2 June 2022 - Version 1.5.0 For the late…

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

Recent released features Features released before 2021 are not listed here. Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, …

Automated Security Testing For REST API's

REST API penetration testing is complex due to continuous changes in existing APIs and newly added APIs. Astra can be used by security engineers or developers as an integral part of their process, so…

🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.

Blankly is an ecosystem for algotraders enabling anyone to build, monetize and scale their trading algorithms for stocks, crypto, futures or forex. The same code can be backtested, paper traded, sand…

SGX-Full-OrderBook-Tick-Data-Trading-Strategy

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Framework to capture the dynamics of high-frequency limit order books. In this project I used machine learning methods to capture the high-frequency limit order book dynamics and simple trading strat…

Algorithmic Trading in Python with Machine Learning

With PyBroker, you'll have all the tools you need to create winning trading strategies backed by data and machine learning. Start using PyBroker today and take your trading to the next level! Or you …

Django email backends and webhooks for Amazon SES, Brevo (Sendinblue), MailerSend, Mailgun, Mailjet, Postmark, Postal, Resend, SendGrid, SparkPost and more

Anymail currently supports these ESPs: Anymail includes: Here's how to send a message. This example uses Mailgun, but you can substitute Mailjet or Postmark or SendGrid or SparkPost or any other supp…

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. T…

Chatbot in 200 lines of code using TensorLayer

This is a 200 lines implementation of Twitter/Cornell-Movie Chatbot, please read the following references before you read the code:

Fastapi mail system sending mails(individual, bulk) attachments(individual, bulk)

The fastapi-mail is a simple lightweight mail system, for sending emails and attachments(individual && bulk) The key features are: Contributions of any kind are welcome!

Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

A light-weight, modular, message representation and mail delivery framework for Python.

To classify video into various classes using keras library with tensorflow as back-end.

To classify videos into various classes using keras library with tensorflow as back-end. I have taken 5 classes from sports 1M dataset like unicycling, marshal arts, dog agility, jetsprint and clay p…

Investment portfolio and stocks analyzing tools for Python with free historical data

...entities should not be multiplied without necessity -- William of Ockham (c. 1287–1347) For many countries (China, USA, United Kingdom, European Union, Russia, Israel etc.): Get the main parameter…

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

Asynchronous Advantage Actor-Critic (A3C) The A3C algorithm was released by Google’s DeepMind group earlier this year, and it made a splash by essentially obsoleting DQN. It was faster, simpler, more…

Scraps historical stock market data from Yahoo Finance (https://finance.yahoo.com/)

I am working on command line argument version where you will be able to download selected stocks with this snippet too. This script can run on multiple Operating Systems. Follow the instructions ment…

Develop a full-stack web application with Python and Flask

Python is a flexible language that can be used for much more than just script development. By knowing how the Python RESTful APIs work, you can build a powerful backend for web applications and mobil…

Vehicle_Collision_Prediction_Using_CNN-LSTMs

Predict Vehicle collision moments before it happens in Carla!. CNN and LSTM hybrid architecture is used to understand a series of images.

A good Bond reference is always due. The data is collected for both the safe and risky classes with 7000 sequences each. Each Sequence consist of 8 images before the collision. The collected images w…

The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

My Projects, Kaggle Competitions and implementation of some popularly known machine learning algorithms. I have also included a list of research papers I have curated in the feild of ML/DL/AI. Follow…

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I develo…

Simple Stock Investment Recommendation System based on Machine-Learning algorithms for prediction and Twitter Sentiment Analysis.

Fist I set up the model for the forecast: Finally I apply the LinearRegression algorithm to the data set and then I plot it to see the representation of the forecast made.

My Attempt(s) In The World Of ML/DL....

Loose path: Understanding Math is pivotal. You can never be a good Machine Learning Scientistby skipping the Math. Python - Any one, both courses are equally good Algorithms Since you'll be coding a …

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

The solutions uploaded in this repository are only for reference when you got stuck somewhere. Please don't use these solutions to pass programming assignments.

Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021

Most of the previous works in visual understanding, rely solely on understanding the "what" (e.g., object recognition) and "where" (e.g., event localization), which in some cases, fails to describe…

Scrape, analyze & visualize stock market data for the S&P500 using Python. Build a basic trading strategy using machine learning to assess company performance and determine buy, sell, hold. Read me & instructions available in Spanish. This is a working repo, with plans to expand the project from technical analysis to fundamental analysis.

Use Python to scrape data and join with financial data from Yahoo Finance (or another finance) API. Use data manipulation and visualization for financial and investment analysis (i.e. compare rates o…

[TGRS 2020] Official Tensorflow implementation for Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network

With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detecti…

The LSTM model generates captions for the input images after extracting features from pre-trained VGG-16 model. (Computer Vision, NLP, Deep Learning, Python)

This is the first step of data pre-processing. The captions contain regular expressions, numbers and other stop words which need to be cleaned before they are fed to the model for further training. T…

Image Captioning: Implementing the Neural Image Caption Generator with python

Image captioning is describing an image fed to the model. The task of object detection has been studied for a long time but recently the task of image captioning is coming into light. This repository…

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