# numpy (57)

**The 20 Python Packages You Need For Machine Learning and Data Science**- Oct 14, 2021.

Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.**Top 10 Python Libraries Data Scientists should know in 2021**- Mar 24, 2021.

So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.**Essential Math for Data Science: Introduction to Matrices and the Matrix Product**- Feb 5, 2021.

As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.**Build Your First Data Science Application**- Feb 4, 2021.

Check out these seven Python libraries to make your first data science MVP application.**Matrix Decomposition Decoded**- Dec 11, 2020.

This article covers matrix decomposition, as well as the underlying concepts of eigenvalues (lambdas) and eigenvectors, as well as discusses the purpose behind using matrix and vectors in linear algebra.**Essential Math for Data Science: Integrals And Area Under The Curve**- Nov 25, 2020.

In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.**Which methods should be used for solving linear regression?**- Sep 2, 2020.

As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.**KDnuggets™ News 20:n26, Jul 8: Speed up Your Numpy and Pandas; A Layman’s Guide to Data Science; Getting Started with TensorFlow 2**- Jul 8, 2020.

Speed up your Numpy and Pandas with NumExpr Package; A Layman's Guide to Data Science. Part 3: Data Science Workflow; Getting Started with TensorFlow 2; Feature Engineering in SQL and Python: A Hybrid Approach; Deploy Machine Learning Pipeline on AWS Fargate**Speed up your Numpy and Pandas with NumExpr Package**- Jul 1, 2020.

We show how to significantly speed up your mathematical calculations in Numpy and Pandas using a small library.**Faster machine learning on larger graphs with NumPy and Pandas**- May 27, 2020.

One of the most exciting features of StellarGraph 1.0 is a new graph data structure — built using NumPy and Pandas — that results in significantly lower memory usage and faster construction times.**Dimensionality Reduction with Principal Component Analysis (PCA)**- May 21, 2020.

This article focuses on design principles of the PCA algorithm for dimensionality reduction and its implementation in Python from scratch.**Complex logic at breakneck speed: Try Julia for data science**- May 20, 2020.

We show a comparative performance benchmarking of Julia with an equivalent Python code to show why Julia is great for data science and machine learning.**Sparse Matrix Representation in Python**- May 19, 2020.

Leveraging sparse matrix representations for your data when appropriate can spare you memory storage. Have a look at the reasons why, see how to create sparse matrices in Python using Scipy, and compare the memory requirements for standard and sparse representations of the same data.**Dive Into Deep Learning: The Free eBook**- Apr 16, 2020.

This freely available text on deep learning is fully interactive and incredibly thorough. Check out "Dive Into Deep Learning" now and increase your neural networks theoretical understanding and practical implementation skills.**Build an Artificial Neural Network From Scratch: Part 2**- Mar 20, 2020.

The second article in this series focuses on building an Artificial Neural Network using the Numpy Python library.**How to Convert a Picture to Numbers**- Jan 6, 2020.

Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy.**Build an Artificial Neural Network From Scratch: Part 1**- Nov 1, 2019.

This article focused on building an Artificial Neural Network using the Numpy Python library.**KDnuggets™ News 19:n32, Aug 28: Handy SQL Features for Data Scientists; Nothing but NumPy: Creating Neural Networks with Computational Graphs**- Aug 28, 2019.

Most useful SQL features for Data Scientist; Excellent tutorial on creating neural nets from scratch with Numpy; TensorFlow 2.0 highlights, explained; How to sell your boss on Data Analytics; and more.**Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch**- Aug 23, 2019.

Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.**Top KDnuggets tweets, Jul 24-30: Nothing but NumPy: Understanding and Creating Neural Nets w. Computational Graphs from Scratch; How Netflix works**- Jul 31, 2019.

How Netflix works: the (hugely simplified) complex stuff that happens every time; Top Certificates and Certifications in Analytics, Data Science, ML; Nothing but NumPy: Understanding &Creating Neural Networks with Computation.**One Simple Trick for Speeding up your Python Code with Numpy**- Jun 19, 2019.

Looping over Python arrays, lists, or dictionaries, can be slow. Thus, vectorized operations in Numpy are mapped to highly optimized C code, making them much faster than their standard Python counterparts.**Become a Pro at Pandas, Python’s Data Manipulation Library**- Jun 13, 2019.

Pandas is one of the most popular Python libraries for cleaning, transforming, manipulating and analyzing data. Learn how to efficiently handle large amounts of data using Pandas.**7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition**- Jun 3, 2019.

This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!**Unleash a faster Python on your data**- Apr 18, 2019.

Intel’s optimized Python packages deliver quick repeatable results compared to standard Python packages. Intel offers optimized Scikit-learn, Numpy, and SciPy to help data scientists get rapid results on their Intel® hardware. Download now.**Feature Reduction using Genetic Algorithm with Python**- Mar 25, 2019.

This tutorial discusses how to use the genetic algorithm (GA) for reducing the feature vector extracted from the Fruits360 dataset in Python mainly using NumPy and Sklearn.**Artificial Neural Networks Optimization using Genetic Algorithm with Python**- Mar 18, 2019.

This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.**Neural Networks with Numpy for Absolute Beginners — Part 2: Linear Regression**- Mar 7, 2019.

In this tutorial, you will learn to implement Linear Regression for prediction using Numpy in detail and also visualize how the algorithm learns epoch by epoch. In addition to this, you will explore two layer Neural Networks.**Neural Networks with Numpy for Absolute Beginners: Introduction**- Mar 5, 2019.

In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.**Artificial Neural Network Implementation using NumPy and Image Classification**- Feb 21, 2019.

This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset**Python Data Science for Beginners**- Feb 20, 2019.

Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.**Top 10 Python Data Science Libraries**- Nov 16, 2018.

The third part of our series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.**Notes on Feature Preprocessing: The What, the Why, and the How**- Oct 26, 2018.

This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.**Basic Image Data Analysis Using Python – Part 3**- Sep 28, 2018.

Accessing the internal component of digital images using Python packages becomes more convenient to help understand its properties, as well as nature.**KDnuggets™ News 18:n30, Aug 8: Iconic Data Visualisation; Data Scientist Interviews Demystified; Simple Statistics in Python**- Aug 8, 2018.

Also: Selecting the Best Machine Learning Algorithm for Your Regression Problem; From Data to Viz: how to select the the right chart for your data; Only Numpy: Implementing GANs and Adam Optimizer using Numpy; Programming Best Practices for Data Science**Only Numpy: Implementing GANs and Adam Optimizer using Numpy**- Aug 6, 2018.

This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.**Basic Image Processing in Python, Part 2**- Jul 17, 2018.

We explain how to easily access and manipulate the internal components of digital images using Python and give examples from satellite image processing.**Basic Image Data Analysis Using Numpy and OpenCV – Part 1**- Jul 10, 2018.

Accessing the internal component of digital images using Python packages becomes more convenient to understand its properties as well as nature.**Top 20 Python Libraries for Data Science in 2018**- Jun 27, 2018.

Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.**Unleash a faster Python on Your Data.**- May 10, 2018.

Get real performance results and download the free Intel(r) Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more.**Boost your data science skills. Learn linear algebra.**- May 3, 2018.

The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.**KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric**- May 2, 2018.

Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial**Building Convolutional Neural Network using NumPy from Scratch**- Apr 26, 2018.

In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.**Why You Should Start Using .npy Files More Often**- Apr 3, 2018.

In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.**Top KDnuggets tweets, Jan 3-9: A collection of Jupyter notebooks NumPy, Pandas, matplotlib, basic #Python #MachineLearning**- Jan 10, 2018.

Artificial General Intelligence (AGI) in less than 50 years; Top KDnuggets tweets: 10 Free Must-Read Books for #MachineLearning and #DataScience; The Art of Learning #DataScience; Supercharging Visualization with Apache Arrow; Docker for #DataScience**Why You Should Forget ‘for-loop’ for Data Science Code and Embrace Vectorization**- Nov 29, 2017.

Data science needs fast computation and transformation of data. NumPy objects in Python provides that advantage over regular programming constructs like for-loop. How to demonstrate it in few easy lines of code?**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