Sep
2018 Oct Tutorials, Overviews
All (116) | Courses, Education (10) | Meetings (9) | News, Features (16) | Opinions (30) | Top Stories, Tweets (11) | Tutorials, Overviews (32) | Webcasts & Webinars (8)
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How Machines Understand Our Language: An Introduction to Natural Language Processing - Oct 31, 2018.
The applications of NLP are endless. This is how a machine classifies whether an email is spam or not, if a review is positive or negative, and how a search engine recognizes what type of person you are based on the content of your query to customize the response accordingly. - Introduction to Deep Learning with Keras - Oct 29, 2018.
In this article, we’ll build a simple neural network using Keras. Now let’s proceed to solve a real business problem: an insurance company wants you to develop a model to help them predict which claims look fraudulent.
- Are you buying an apartment? How to hack competition in the real estate market - Oct 26, 2018.
Many real estate developers use online systems for sales. Things become interesting when all available data is monitored on a weekly basis, and sales progress is analysed.
- 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.
- Naive Bayes from Scratch using Python only – No Fancy Frameworks - Oct 25, 2018.
We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.
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Named Entity Recognition and Classification with Scikit-Learn - Oct 25, 2018.
Named Entity Recognition and Classification is a process of recognizing information units like names, including person, organization and location names, and numeric expressions from unstructured text. The goal is to develop practical and domain-independent techniques in order to detect named entities with high accuracy automatically. - Implementing Automated Machine Learning Systems with Open Source Tools - Oct 25, 2018.
What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.
- Generative Adversarial Networks – Paper Reading Road Map - Oct 24, 2018.
To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.
- Building a Question-Answering System from Scratch - Oct 24, 2018.
This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.
- Introduction to Active Learning - Oct 23, 2018.
An extensive overview of Active Learning, with an explanation into how it works and can assist with data labeling, as well as its performance and potential limitations.
- Get a 2–6x Speed-up on Your Data Pre-processing with Python - Oct 23, 2018.
Get a 2–6x speed-up on your pre-processing with these 3 lines of code!
- Beginner Data Visualization & Exploration Using Pandas - Oct 22, 2018.
This tutorial will offer a beginner guide into how to get around with Pandas for data wrangling and visualization.
- The Intuitions Behind Bayesian Optimization with Gaussian Processes - Oct 19, 2018.
Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.
- Apache Spark Introduction for Beginners - Oct 18, 2018.
An extensive introduction to Apache Spark, including a look at the evolution of the product, use cases, architecture, ecosystem components, core concepts and more.
- The Main Approaches to Natural Language Processing Tasks - Oct 17, 2018.
Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.
- Adversarial Examples, Explained - Oct 16, 2018.
Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities.
- Applied Data Science: Solving a Predictive Maintenance Business Problem Part 3 - Oct 16, 2018.
In this post we will expand our analysis to multiple variables and then see how intuitions we develop during the exploration phase, can lead to generating new features for modelling.
- GitHub Python Data Science Spotlight: High Level Machine Learning & NLP, Ensembles, Command Line Viz & Docker Made Easy - Oct 16, 2018.
This post spotlights 5 data science projects, all of which are open source and are present on GitHub repositories, focusing on high level machine learning libraries and low level support tools.
- 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.
- 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.
Interpretable Machine Learning: The Free eBook