2019 Feb Tutorials, Overviews
All (102) | Courses, Education (5) | Meetings (18) | News (14) | Opinions (30) | Top Stories, Tweets (9) | Tutorials, Overviews (22) | Webcasts & Webinars (4)
- TensorFlow.js: Machine learning for the web and beyond - Feb 28, 2019.
TensorFlow.js brings TensorFlow and Keras to the the JavaScript ecosystem, supporting both Node.js and browser-based applications. Read a summary of the paper which describes the design, API, and implementation of TensorFlow.js.
- How to do Everything in Computer Vision - Feb 27, 2019.
The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!
- Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters - Feb 27, 2019.
Google’s BERT algorithm has emerged as a sort of “one model to rule them all.” BERT builds on two key ideas that have been responsible for many of the recent advances in NLP: (1) the transformer architecture and (2) unsupervised pre-training.
- Simple Yet Practical Data Cleaning Codes - Feb 26, 2019.
Real world data is messy and needs to be cleaned before it can be used for analysis. Industry experts say the data preprocessing step can easily take 70% to 80% of a data scientist's time on a project.
- What are Some “Advanced” AI and Machine Learning Online Courses? - Feb 22, 2019.
Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?
- Deep Learning for Natural Language Processing (NLP) – using RNNs & CNNs - Feb 21, 2019.
We investigate several Natural Language Processing tasks and explain how Deep Learning can help, looking at Language Modeling, Sentiment Analysis, Language Translation, and more.
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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 - State of the art in AI and Machine Learning – highlights of papers with code - Feb 20, 2019.
We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.
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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. - Running R and Python in Jupyter - Feb 19, 2019.
The Jupyter Project began in 2014 for interactive and scientific computing. Fast forward 5 years and now Jupyter is one of the most widely adopted Data Science IDE's on the market and gives the user access to Python and R
- Are BERT Features InterBERTible? - Feb 19, 2019.
This is a short analysis of the interpretability of BERT contextual word representations. Does BERT learn a semantic vector representation like Word2Vec?
- How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
- A comprehensive survey on graph neural networks - Feb 15, 2019.
This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.
- Decision Trees — An Intuitive Introduction - Feb 14, 2019.
An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more.
- An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.
- A Quick Guide to Feature Engineering - Feb 11, 2019.
Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field.
- Understanding Gradient Boosting Machines - Feb 6, 2019.
However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique.
- 6 Data Visualization Disasters – How to Avoid Them - Feb 5, 2019.
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid.
- From Good to Great Data Science, Part 1: Correlations and Confidence - Feb 5, 2019.
With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes.
- Intuitive Visualization of Outlier Detection Methods - Feb 5, 2019.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own.
- Using Caret in R to Classify Term Deposit Subscriptions for a Bank - Feb 4, 2019.
This article uses direct marketing campaign data from a Portuguese banking institution to predict if a customer will subscribe for a term deposit. We’ll be working with R’s Caret package to achieve this.
- Trending Deep Learning Github Repositories - Feb 1, 2019.
Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.
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