# Tag: numpy

**Getting Started with Python for Data Analysis**- Jul 5, 2017.

A guide for beginners to Python for getting started with data analysis.

**Working With Numpy Matrices: A Handy First Reference**- Mar 10, 2017.

This introductory tutorial does a great job of outlining the most common Numpy array creation and manipulation functionality. A good post to keep handy while taking your first steps in Numpy, or to use as a handy reminder.**Top KDnuggets tweets, Dec 7-13: Want to learn Numpy? A Github repo of Numpy learning exercises**- Dec 14, 2016.

Also Deep Learning Roadmap: "Which paper should I start reading from?"; Free ebooks: #MachineLearning with #Python and Practical Data Analysis; Daily plan for studying to become a Google software engineer.**2 must-have tools for blazing fast Python performance**- Sep 15, 2016.

Intel has two must-have, highly optimized tools to help you get faster performance out of the box - with the least amount of effort.**Deep Residual Networks for Image Classification with Python + NumPy**- Jul 7, 2016.

This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.**Top KDnuggets tweets, Jun 1-7: “Deep” vs “Regular” Machine Learning; Introduction to Scientific Python – NumPy**- Jun 8, 2016.

How to Build Your Own #DeepLearning Box; What is the Difference Between #DeepLearning and "Regular" #MachineLearning? Data Science of #Variable Selection: A Review; Why choose #Python for #MachineLearning?**KDnuggets™ News 16:n20, Jun 8: R, Python Duel for 1st Place; “Regular” Machine Learning vs Deep Learning; Numpy Intro**- Jun 8, 2016.

R, Python Duel As Top Analytics, Data Science software; What is the Difference Between Deep Learning and "Regular" Machine Learning; An Introduction to Scientific Python; How to Build Your Own Deep Learning Box**An Introduction to Scientific Python (and a Bit of the Maths Behind It) – NumPy**- Jun 1, 2016.

An introductory overview of NumPy, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.**Top New Features in Orange 3 Data Mining Platform**- Dec 10, 2015.

The main technical advantage of Orange 3 is its integration with NumPy and SciPy libraries. Other improvements include reading online data, working through queries for SQL and pre-processing.**Top /r/MachineLearning Posts, June: Neural Network Generated Images, Free Data Science Books, Super Mario World**- Jul 2, 2015.

Generating images with neural networks, free data science books, machine learning for playing Mario, implementing neural networks in Python, and video generation based on terms were all covered this month on /r/MachineLearning.**Top KDnuggets tweets, Jun 9-10: Numeric Matrix Manipulation: Cheat Sheet; The First Law of Data Science**- Jun 11, 2014.

Also - The First Law of Data Science: Do Umbrellas Cause Rain? ; Tell Your Kids to be Data Scientists - Not Doctors; DLib Library for Machine Learning**Top KDnuggets tweets, Jan 29-30: Visual.ly Data Visualization Catalog; 100 numpy exercises, from Novice to Expert Data Scientists**- Jan 31, 2014.

Visual.ly Data Visualization Catalog help you choose the right visualization; 100 numpy exercises, from Novice to Expert Data Scientists; R vs Python Duel, Contest 1A - download, process 2GB census data; Online course: More Data Mining with Weka