2018 Sep Tutorials, Overviews
All (106) | Courses, Education (5) | Meetings (12) | News, Features (13) | Opinions (28) | Top Stories, Tweets (9) | Tutorials, Overviews (29) | Webcasts & Webinars (10)
- 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.
- Introduction to Deep Learning - Sep 28, 2018.
I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.
- Visualising Geospatial data with Python using Folium - Sep 27, 2018.
Folium is a powerful data visualization library in Python that was built primarily to help people visualize geospatial data. With Folium, one can create a map of any location in the world if its latitude and longitude values are known. This guide will help you get started.
- Raspberry Pi IoT Projects for Fun and Profit - Sep 27, 2018.
In this post, I will explain how to run an IoT project from the command line, without graphical interface, using Ubuntu Core in a Raspberry Pi 3.
- Introducing Path Analysis Using R - Sep 27, 2018.
Path analysis is an extension of multiple regression. It allows for the analysis of more complicated models.
- Power Laws in Deep Learning 2: Universality - Sep 26, 2018.
It is amazing that Deep Neural Networks display this Universality in their weight matrices, and this suggests some deeper reason for Why Deep Learning Works.
- Introducing VisualData: A Search Engine for Computer Vision Datasets - Sep 26, 2018.
Instead of building your own dataset, there already exists a rich collection of computer vision datasets contributed by academic researchers, hobbyists and companies.
- The Whys and Hows of Web Scraping – A Lethal Weapon in Your Data Arsenal - Sep 25, 2018.
We breakdown the various aspects of web scraping, from why businesses need to do it, to instructions on how to go about acquiring this data with PromptCloud - a pioneer in Data as Service solutions with specialization in large-scale and custom web data extraction.
- Unfolding Naive Bayes From Scratch - Sep 25, 2018.
Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!
- “Auto-What?” – A Taxonomy of Automated Machine Learning - Sep 25, 2018.
Automated machine learning is a rapidly developing segment of artificial intelligence - it’s time to define what an AutoML product is so end-users can compare product capabilities intelligently.
- Deep Learning Framework Power Scores 2018 - Sep 24, 2018.
Who’s on top in usage, interest, and popularity?
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6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study - Sep 20, 2018.
Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process. - Power Laws in Deep Learning - Sep 20, 2018.
In pretrained, production quality DNNs, the weight matrices for the Fully Connected (FC ) layers display Fat Tailed Power Law behavior.
- Data Augmentation For Bounding Boxes: Rethinking image transforms for object detection - Sep 19, 2018.
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
- SQL Case Study: Helping a Startup CEO Manage His Data - Sep 19, 2018.
In this tutorial, you will learn how to create a table, insert values into it, use and understand some data types, use SELECT statements, UPDATE records, use some aggregate functions, and more.
- Everything You Need to Know About AutoML and Neural Architecture Search - Sep 13, 2018.
So how does it work? How do you use it? What options do you have to harness that power today? Here’s everything you need to know about AutoML and NAS.
- Iterative Initial Centroid Search via Sampling for k-Means Clustering - Sep 12, 2018.
Thinking about ways to find a better set of initial centroid positions is a valid approach to optimizing the k-means clustering process. This post outlines just such an approach.
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Machine Learning Cheat Sheets - Sep 11, 2018.
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus. - Machine Learning for Text Classification Using SpaCy in Python - Sep 11, 2018.
In this post, we will demonstrate how text classification can be implemented using spaCy without having any deep learning experience.
- Object Detection and Image Classification with YOLO - Sep 10, 2018.
We explain object detection, how YOLO algorithm can help with image classification, and introduce the open source neural network framework Darknet.
- Training with Keras-MXNet on Amazon SageMaker - Sep 10, 2018.
In this post, you will learn how to train Keras-MXNet jobs on Amazon SageMaker. I’ll show you how to build custom Docker containers for CPU and GPU training, configure multi-GPU training, pass parameters to a Keras script, and save the trained models in Keras and MXNet formats.
- 5 Things to Know About A/B Testing - Sep 7, 2018.
This article presents 5 things to know about A/B testing, from appropriate sample sizes, to statistical confidence, to A/B testing usefulness, and more.
- Ultimate Guide to Getting Started with TensorFlow - Sep 6, 2018.
Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.
- Essential Math for Data Science: ‘Why’ and ‘How’ - Sep 6, 2018.
It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.
- Data Science Cheat Sheet - Sep 6, 2018.
Check out this new data science cheat sheet, a relatively broad undertaking at a novice depth of understanding, which concisely packs a wide array of diverse data science goodness into a 9 page treatment.
- Deep Learning for NLP: An Overview of Recent Trends - Sep 5, 2018.
A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.
- Financial Data Analysis – Data Processing 1: Loan Eligibility Prediction - Sep 4, 2018.
In this first part I show how to clean and remove unnecessary features. Data processing is very time-consuming, but better data would produce a better model.
- OLAP queries in SQL: A Refresher - Sep 3, 2018.
Based on the recent book - Principles of Database Management - The Practical Guide to Storing, Managing and Analyzing Big and Small Data - this post examines how OLAP queries can be implemented in SQL.
- An End-to-End Project on Time Series Analysis and Forecasting with Python - Sep 3, 2018.
Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series.
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