Temporal Convolutional Nets (TCNs) Take Over from RNNs for NLP Predictions
Our starting assumption that sequence problems (language, speech, and others) are the natural domain of RNNs is being challenged. Temporal Convolutional Nets (TCNs) which are our workhorse CNNs with a few new features are outperforming RNNs on major applications today. Looks like RNNs may well be history.
Markov Chains in Python: Beginner Tutorial
A Markov chain is a mathematical system usually defined as a collection of random variables, that transition from one state to another according to certain probabilistic rules. These set of transition satisfies the Markov Property, which states that the probability of transitioning to any particular state is dependent solely on the current state and time elapsed, and not on the sequence of state that preceded it. This unique characteristic of Markov processes render them memoryless.
50+ Useful Machine Learning & Prediction APIs, 2018 Edition
Face and Image Recognition
1. Animetrics Face Recognition
3. Eyedea Recognition
7. Google Cloud Vision API
8. IBM Watson Visual Recognition
11. Microsoft Cognitive Service – Computer Vision
12. Skybiometry Face Detection and Recognition
Text Analysis, NLP, Sentiment Analysis
14. Diffbot Analyze
15. Free Natural Language Processing Service
16. Google Cloud Natural Language API
17. Watson Natural Language Understanding
18. MeaningCloud Text Classification
19. Microsoft Cognitive Service – Text Analytics
22. Thomson Reuters Open Calais
23. Yactraq Speech2Topics
24. Google Cloud Translation
25. Google Cloud SPEECH-TO-TEXT
26. IBM Watson Language Translator
28. WritePath Translation
30. IBM Watson Conversation
31. IBM Watson Speech
Machine Learning and Prediction
32. Amazon Machine Learning
34. Google Cloud Prediction
37. IBM Watson Retrieve and Rank
39. Microsoft Azure Cognitive Service API
40. Microsoft Azure Anomaly Detection API
41. Microsoft Cognitive Service – QnA Maker
42. Microsoft Cognitive Service – Speaker Recognition
47. RxNLP – Cluster Sentences and Short Texts
Data Science vs Machine Learning vs Data Analytics vs Business Analytics
This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI.
Jupyter Notebook for Beginners: A Tutorial
The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.
How to do Repeated Measures ANOVAs in R
There is now a tremendous amount of data showing the inadequacy of ANOVAs as a statistical procedure (Camilli, 1987; Levy, 1978; Vasey, 1987; Chang, 2009). Instead, many papers suggest moving toward the mixed-modelling framework (Kristensen, 2004; Jaeger, 2008), which was shown to be more flexible, accurate, powerful and suited for psychological data. Using this framework, we will see how we can very simply answer our questions with R and the psycho package.
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Implementation and Interpretation of Control Charts in R
In this article, we will go through the implementation and interpretation of Control Charts, popularly used during Six Sigma DMAIC projects. Six Sigma at many organizations means a measure of quality that strives for near perfection. Six Sigma is a data-driven approach and methodology for eliminating defects (driving toward six standard deviations between the mean and the nearest specification limit) in any process. Six Sigma DMAIC is the problem-solving methodology. It consists of five Phases: Define, Measure, Analyse, Improve and Control.
An intuitive, visual guide to copulas
People seemed to enjoy my intuitive and visual explanation of Markov chain Monte Carlo so I thought it would be fun to do another one, this time focused on copulas.
Getting Started with spaCy for Natural Language Processing
spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.