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automatic-interpretation-of-otoliths-using-deep-learning

Recent advances in machine learning have brought forth methods that have been remarkably successful in a variety of settings, most notably in image analysis. These methods are now being applied to data analysis in marine sciences, where they have the potential to automate analysis that previously required manual curation. Here we adapt a machine learning model intended for object recognition to the task of estimating age from otolith images. The model is trained and validated on a collection of otolith images from Greenland halibut. We show that the precision of the model's age estimates is comparable to and may even surpass that of human experts. Age reading from otoliths is an important element in the management of many marine stocks, and automating this analysis is an important step to ensure consistency, lower cost, and increase scale.

crypto_trader

Q-Learning Based Cryptocurrency Trader and Portfolio Optimizer for the Poloniex Exchange

helloworld

this is a test!

investing-bot

Predictive model for the quantitative analysis of stocks using machine learning AI

machinelearning

neusoft

notes

ooad

pythonandmachinelearningforstocks

Using Web Scraping, Sentiment Analysis and Machine Learning to predict Stock prices and trends in Python

quant

Quantitative Finance and Algorithmic Trading

redditsentimentanalysisfinance

Test using NLTK to see how well I could train a machine learning algorithm to get the sentiment of reddit for different stocks

sector-etf-recommender

This is a machine learning project to recommend SPDR Sector ETF stocks that maximise daily returns.

software.engineering

stanford-project-predicting-stock-prices-with-lstm-networks

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

stock-market-prediction-using-machine-learning-models

We explore how different machine learning techniques can be leveraged to pick stocks that can be sold for profit. Studying and analyzing past data and using it to make predictions will help investors make informed decisions about buying or selling stocks thus giving them an edge in the market.

stock-recommendation

Welcome to our Big Data & Analytics project - a project about stock recommendations using big data. Stocks are traded every day, and traders use information about stocks, such as fundamental data to make decisions on whether to buy, sell or hold a stock. This information is available in large quantities, so it is impossible for the individual trader to process all of this information and include it in stock buying decisions. In order to solve this problem we built a Machine Learning Algorithm with Python to predict as to whether it is worth buying, selling or holding a share today to make a profit in a certain amout of time.

stock-return-prediction-using-knn-svm-guassian-process-adaboost-tree-regression-and-qda

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

stocks_ml

Analyzing stocks with machine learning

stocks_trading

In this repository placed programs which designed to predict the shares of companies. This programs are based on some machine learning methods

tensorflow_tutorials

From the basics to slightly more interesting applications of Tensorflow

tmp

universal-portfolios

Collection of algorithms for online portfolio selection