{"id":1446,"date":"2017-11-06T02:29:35","date_gmt":"2017-11-06T02:29:35","guid":{"rendered":"http:\/\/intelligentonlinetools.com\/blog\/?p=1446"},"modified":"2017-11-10T01:28:50","modified_gmt":"2017-11-10T01:28:50","slug":"10-new-top-resources-on-machine-learning-from-around-the-web","status":"publish","type":"post","link":"http:\/\/intelligentonlinetools.com\/blog\/2017\/11\/06\/10-new-top-resources-on-machine-learning-from-around-the-web\/","title":{"rendered":"10 New Top Resources on Machine Learning from Around the Web"},"content":{"rendered":"

For this post I put new and most interesting machine learning<\/b> resources that I recently found on the web. This is the list of useful resources in such areas like stock market forecasting<\/b>, text mining<\/b>, deep learning<\/b>, neural networks<\/b> and getting data from Twitter<\/b>. Hope you enjoy the reading.<\/p>\n

1. Stock market forecasting<\/b> with prophet<\/a> – this post belongs to series of posts about using Prophet which is the tool for producing high quality forecasts for time series data<\/b> that has multiple seasonality with linear or non-linear growth. You will find here different techniques for stock data forecasting.
\nProphet<\/a> is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI.<\/p>\n

2. Python For Finance: Algorithmic Trading<\/a> – Another post about stock data analysis<\/strong> with python. This tutorial introduces you to algorithmic trading, and much more.<\/p>\n

3. Recommendation and trend analysis is interesting topic. You can read this post to find out how to improve algorithms: recommendation-engine-for-trending-products-in-python<\/a> In this post author is proposing new trending products algorithm<\/strong> in order to increase serendipity. This will allow to show to user something the user would not expect, but still could find interesting. <\/p>\n

4. Word2Vec word embedding tutorial in Python and TensorFlow<\/a> This tutorial is covering \u201cWord2Vec<\/strong>\u201d technique. This methodology is used in NLP to efficiently convert words into numeric vectors.<\/p>\n

5. Best Practices for Document Classification with Deep Learning<\/a> In this post you will find review of some best practices how to use deep learning<\/strong> for text classification<\/strong>. From the examples in this post you will discover different type of Convolutional Neural Networks (CNN)<\/strong> architecture.<\/p>\n

6. How to Develop a Deep Learning Bag-of-Words Model for Predicting Movie Review Sentiment<\/a> In this post you will find how to use deep learning<\/strong> model for sentiment analysis<\/strong>. The model is simple feedforward network<\/strong> with fully connected layers. <\/p>\n

7. How to Clean Text for Machine Learning with Python<\/a> – Here you will find great and complete tutorial for text preprocessing<\/strong> with python. Also links to resources for further learning are provided too.<\/p>\n

8. Gathering Tweets with Python<\/a>. This tutorial guides you in setting up a system for collecting Tweets<\/strong>.<\/p>\n

9. Twitter Data Mining: A Guide To Big Data Analytics Using Python<\/a> Here you can also find how to connect to Twitter<\/strong> and extract some tweets.<\/p>\n

10. Stream data from Twitter using Python<\/a> This post will show you how to get all identification information required for connecting to Twitter. Also you will find here how to receive tweets via the stream from Twitter<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

For this post I put new and most interesting machine learning resources that I recently found on the web. This is the list of useful resources in such areas like stock market forecasting, text mining, deep learning, neural networks and getting data from Twitter. Hope you enjoy the reading. 1. Stock market forecasting with prophet … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":[]},"categories":[9,10,3],"tags":[15,17,18,27,22,14,16],"jetpack_publicize_connections":[],"yoast_head":"\n10 New Top Resources on Machine Learning from Around the Web - Machine Learning Applications<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/intelligentonlinetools.com\/blog\/2017\/11\/06\/10-new-top-resources-on-machine-learning-from-around-the-web\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"10 New Top Resources on Machine Learning from Around the Web - Machine Learning Applications\" \/>\n<meta property=\"og:description\" content=\"For this post I put new and most interesting machine learning resources that I recently found on the web. 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Facebook team have implemented in Prophet two trend models that can cover many applications: a saturating growth model, and a piecewise linear model. [4] With growth model Prophet can be used for prediction growth\/decay - for\u2026","rel":"","context":"In "Machine Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2017\/12\/time-series-analysis-python-300x180.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1178,"url":"http:\/\/intelligentonlinetools.com\/blog\/2017\/05\/14\/time-series-prediction-with-convolutional-neural-networks\/","url_meta":{"origin":1446,"position":1},"title":"Forecasting Time Series Data with Convolutional Neural Networks","date":"May 14, 2017","format":false,"excerpt":"Convolutional neural networks(CNN) is increasingly important concept in computer science and finds more and more applications in different fields. Many posts on the web are about applying convolutional neural networks for image classification as CNN is very useful type of neural networks for image classification. But convolutional neural networks can\u2026","rel":"","context":"In "Artificial Intelligence"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2017\/05\/time-series-LSTM-300x164.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1783,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/01\/19\/machine-learning-stock-prediction-lstm-keras\/","url_meta":{"origin":1446,"position":2},"title":"Machine Learning Stock Prediction with LSTM and Keras","date":"January 19, 2018","format":false,"excerpt":"In this post I will share experiments on machine learning stock prediction with LSTM and Keras with one step ahead. I tried to do first multiple steps ahead with few techniques described in the papers on the web. But I discovered that I need fully understand and test the simplest\u2026","rel":"","context":"In "Machine Learning"","img":{"alt_text":"Forecasting one step ahead LSTM 60","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/01\/Forecasting-one-step-ahead-LSTM-60-3_1_2018.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1754,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/02\/27\/time-series-prediction-lstm-keras\/","url_meta":{"origin":1446,"position":3},"title":"Time Series Prediction with LSTM and Keras for Multiple Steps Ahead","date":"February 27, 2018","format":false,"excerpt":"In this post I will share experiment with Time Series Prediction with LSTM and Keras. LSTM neural network is used in this experiment for multiple steps ahead for stock prices data. The experiment is based on the paper [1]. The authors of the paper examine independent value prediction approach. 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