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Josephmisiti/awesome-machine-learning

Josephmisiti/awesome-machine-learning

https://github.com/josephmisiti/awesome-machine-learning

Related:  Java Libraries and ToolsMachine LearningAlgorithms & Data Structures

lanterna - Easy console text GUI library for Java Welcome Also, when running Lanterna on computers with a graphical environment (such as Windows or Xorg), a bundled terminal emulator written in Swing will be used rather than standard output. This way, you can develop as usual from your IDE (most of them doesn't support ANSI control characters in their output window) and then deploy to your headless server without changing any code. Lanterna is structured into three layers, each built on top of the other and you can easily choose which one fits your needs best. The first is a low level terminal interface which gives you the most basic control of the terminal text area. You can move around the cursor and enable special modifiers for characters put to the screen.

Stanford Large Network Dataset Collection Social networks Networks with ground-truth communities Communication networks Citation networks Collaboration networks Web graphs Golang library data science machine learning The Golang programming language is on a par with Python when it comes to ease of use, but the code compiles to a binary that runs almost as fast as C. So Golang is worth considering for any task that crunches large volumes of data. This is a curated list of Golang libraries useful in data science and related fields. The term ‘data science’ is used here in the broadest sense to cover areas as diverse as: natural language processing.machine learning.advanced mathematics including statistics, probability, algebra and calculus.data extraction, cleaning, normalisation, conversion, reformatting and storage.data visualisation.

Xtext - Language Development Made Easy! Syntax Coloring Out of the box, the editor supports syntax coloring based on the lexical structure and the semantic data of your files. Users are free to customize the highlighting and configure their favorite styles. Content Assist An Xtext editor proposes valid code completions at any place in the document, helping your users with the syntactical details of your language. Validation and Quick Fixes

Metacademy - Level-Up Your Machine Learning Since launching Metacademy, I've had a number of people ask , What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn? Excellent question! My answer: consistently work your way through textbooks. I then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, "Oh, I watch what I eat and consistently exercise."

Example: Huffman Encoding Trees Next: Multiple Representations for Abstract Up: Symbolic Data Previous: Example: Representing Sets This section provides practice in the use of list structure and data abstraction to manipulate sets and trees. The application is to methods for representing data as sequences of ones and zeros (bits). For example, the ASCII standard code used to represent text in computers encodes each character as a sequence of seven bits. Java 8 Part 4 - Concurrency, TLS SNI, and PermGen Java 8 is shipping with a slew of awesome new features that you learned about in parts one and two of this Java 8 series. In part three we wrote about the improvements being made to HashMap and the date and time API, and now, in the fourth (and final) installment, we’ll round out the series by showing some of the other various improvements being shipped with Java 8. Concurrency Update The java.util.concurrent.atomic package has been expanded to include four new classes that allow for concurrent, scalable, and updatable variables.

Becoming a Data Scientist - Curriculum via Metromap ← Pragmatic Perspectives Data Science, Machine Learning, Big Data Analytics, Cognitive Computing …. well all of us have been avalanched with articles, skills demand info graph’s and point of views on these topics (yawn!). One thing is for sure; you cannot become a data scientist overnight. Its a journey, for sure a challenging one. But how do you go about becoming one? Where to start? When do you start seeing light at the end of the tunnel? 7CCSMTSP - Text Searching and Processing January-March 2016, Friday 3-6pm in room S-1.04 NOTE: lecture on 16 March 1-4pm in S-1.04 in replacement of 25 March (Good Friday) Revision lecture on Wednesday 4 May 2016, 3-5pm in room K2.31 Lecturers Maxime Crochemore, email for an appointment, Office hours: Friday 10-noon in S6.33 Ritu Kundu (tutorials), email for a question Robert Mercas (tutorials), email for a question Jakub Radoszewski (tutorials), email for a question Aims This unit is devoted to algorithms processing strings and texts efficiently. These types of algorithms are used for software design in the domains of operating systems utilities, search engines on the Internet, data retrieval systems, analysis of genetic sequences, and natural language processing, for example.

6 Great Java Interview Questions and Answers In Java, each thread has its own stack, including its own copy of variables it can access. When the thread is created, it copies the value of all accessible variables into its own stack. The volatile keyword basically says to the JVM “Warning, this variable may be modified in another Thread”. In all versions of Java, the volatile keyword guarantees global ordering on reads and writes to a variable. This implies that every thread accessing a volatile field will read the variable’s current value instead of (potentially) using a cached value. In Java 5 or later, volatile reads and writes establish a happens-before relationship, much like acquiring and releasing a mutex.

Practical Machine Learning Problems - Machine Learning Mastery What is Machine Learning? We can read authoritative definitions of machine learning, but really, machine learning is defined by the problem being solved. Therefore the best way to understand machine learning is to look at some example problems. In this post we will first look at some well known and understood examples of machine learning problems in the real world. CS276B: Web Search and Mining Christopher Manning and Prabhakar Raghavan Course website from Winter 2003 276B was last offered two years ago in Winter 2002-2003.

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