Sep2018 Oct Tutorials, Overviews
All (57) | Courses, Education (8) | Meetings (5) | News, Features (8) | Opinions, Interviews (15) | Top Stories, Tweets (6) | Tutorials, Overviews (14) | Webcasts & Webinars (1)
- 5 “Clean Code” Tips That Will Dramatically Improve Your Productivity
- Oct 15, 2018.
TL;DR: If it isn’t tested, it’s broken; Choose meaningful names; Classes and functions should be small and obey the Single Responsibility Principle (SRP); Catch and handle exceptions, even if you don’t think you need to; Logs, logs, logs
- Machine learning — Is the emperor wearing clothes?
- Oct 12, 2018.
We take a look at the core concepts of Machine Learning, including the data, algorithm and optimization needed to get you started, with links to additional resources to help enhance your knowledge.
- We Sized Washington’s Edible Marijuana Market Using AI
- Oct 12, 2018.
As Canada legalizes marijuana, it might look to Washington to understand how pot sells. With the RAND Corp., we used machine learning to estimate how much THC- in pot is sold in Washington.
- Using Confusion Matrices to Quantify the Cost of Being Wrong
- Oct 11, 2018.
The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.
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10 Best Mobile Apps for Data Scientist / Data Analysts
- Oct 10, 2018.
A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more. - Preprocessing for Deep Learning: From covariance matrix to image whitening
- Oct 10, 2018.
The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions!
- Building an Image Classifier Running on Raspberry Pi
- Oct 9, 2018.
The tutorial starts by building the Physical network connecting Raspberry Pi to the PC via a router. After preparing their IPv4 addresses, SSH session is created for remotely accessing of the Raspberry Pi. After uploading the classification project using FTP, clients can access it using web browsers for classifying images.
- A Concise Explanation of Learning Algorithms with the Mitchell Paradigm
- Oct 5, 2018.
A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.
- Semantic Segmentation: Wiki, Applications and Resources
- Oct 4, 2018.
An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.
- Society of Machines: The Complex Interaction of Agents
- Oct 4, 2018.
In this new series we’ll focus on collective interaction of two or more machines. This interaction of machines can be among each other or with the environment.
- Linear Regression in the Wild
- Oct 3, 2018.
We take a look at how to use linear regression when the dependent variables have measurement errors.
- Sequence Modeling with Neural Networks – Part I
- Oct 3, 2018.
In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.
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Recent Advances for a Better Understanding of Deep Learning
- Oct 1, 2018.
A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more. - More Effective Transfer Learning for NLP
- Oct 1, 2018.
Until recently, the natural language processing community was lacking its ImageNet equivalent — a standardized dataset and training objective to use for training base models.