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2017 Jul Tutorials, Overviews
http likes 176All (100) | Courses, Education (4) | Meetings (15) | News, Features (16) | Opinions, Interviews (26) | Software (2) | Tutorials, Overviews (30) | Webcasts & Webinars (7)
- The Internet of Things: An Introductory Tutorial Series
- Jul 28, 2017.
In this series of post, the author will be presenting a set of Internet of Things technologies and applications in the form of tutorial in chapter form. Basic concepts are covered with an approachable style, not heaped in technical terms.
- How to squeeze the most from your training data
- Jul 27, 2017.
In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.
- The Machine Learning Abstracts: Classification
- Jul 27, 2017.
Classification is the process of categorizing or “classifying” some items into a predefined set of categories or “classes”. It is exactly the same even when a machine does so. Let’s dive a little deeper.
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Machine Learning Exercises in Python: An Introductory Tutorial Series
- Jul 26, 2017.
This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way. - Introduction to Neural Networks, Advantages and Applications
- Jul 25, 2017.
Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.
- The Truth About Bayesian Priors and Overfitting
- Jul 25, 2017.
Many of the considerations we will run through will be directly applicable to your everyday life of applying Bayesian methods to your specific domain.
- Summary of Unintuitive Properties of Neural Networks
- Jul 24, 2017.
Neural networks work really well on many problems, including language, image and speech recognition. However understanding how they work is not simple, and here is a summary of unusual and counter intuitive properties they have.
- Picking an Optimizer for Style Transfer
- Jul 21, 2017.
Gradient Descent, Adam or Limited-memory Broyden–Fletcher–Goldfarb–Shanno? Which will optimize your style transfer neural network faster and better? Read this post for a data-backed discussion.
- Design by Evolution: How to evolve your neural network with AutoML
- Jul 20, 2017.
The gist ( tl;dr): Time to evolve! I’m gonna give a basic example (in PyTorch) of using evolutionary algorithms to tune the hyper-parameters of a DNN.
- Populating a GRAKN.AI Knowledge Graph with the World
- Jul 20, 2017.
This updated article describes how to move SQL data into a GRAKN.AI knowledge graph.
- Hacking in silico protein engineering with Machine Learning and AI, explained
- Jul 19, 2017.
Proteins are building blocks of all living matter. Although tremendous progress has been made, protein engineering remains laborious, expensive and truly complicated. Here is how Machine Learning can help.
- Road Lane Line Detection using Computer Vision models
- Jul 19, 2017.
A tutorial on how to implement a computer vision data pipeline for road lane detection used by self-driving cars.
- 5 Free Resources for Getting Started with Deep Learning for Natural Language Processing
- Jul 19, 2017.
This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.
- Optimizing Web sites: Advances thanks to Machine Learning
- Jul 17, 2017.
Machine learning has revitalized a nearly dormant method, leading to a powerful approach for optimizing Web pages, finding the best of thousands of alternatives.
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Machine Learning Applied to Big Data, Explained
- Jul 17, 2017.
Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own. - How to Build a Data Science Pipeline
- Jul 14, 2017.
Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.
- Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity
- Jul 13, 2017.
There is a growing community around creating tools that automate many machine learning tasks, as well as other tasks that are part of the machine learning workflow. The paradigm that encapsulates this idea is often referred to as automated machine learning.
- CAN (Creative Adversarial Network) - Explained
- Jul 12, 2017.
GANs (Generative Adversarial Networks), a type of Deep Learning networks, have been very successful in creating non-procedural content. This work explores the possibility of machine generated creative content.
- The Guerrilla Guide to Machine Learning with Julia
- Jul 12, 2017.
This post is a lean look at learning machine learning with Julia. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.
- Medical Image Analysis with Deep Learning , Part 4
- Jul 11, 2017.
This is the fourth installment of this series, and covers medical images and their components, medical image formats and their format conversions. The goal is to develop knowledge to help us with our ultimate goal — medical image analysis with deep learning.
- What Are Artificial Intelligence, Machine Learning, and Deep Learning?
- Jul 10, 2017.
AI and Machine Learning have become mainstream, and people know shockingly little about it. Here is an explainer and useful references.
- 5 Free Resources for Getting Started with Self-driving Vehicles
- Jul 10, 2017.
This is a short list of 5 resources to help newcomers find their bearings when learning about self-driving vehicles, all of which are free. This should be sufficient to learn the basics, and to learn where to look next for further instruction.
- Exploratory Data Analysis in Python
- Jul 7, 2017.
We view EDA very much like a tree: there is a basic series of steps you perform every time you perform EDA (the main trunk of the tree) but at each step, observations will lead you down other avenues (branches) of exploration by raising questions you want to answer or hypotheses you want to test.
- Inference Made Simple – Applying the reasoning power of GRAKN.AI to find new knowledge about the world
- Jul 7, 2017.
This article aims to provide an overview of getting started with GRAKN.AI, and provides a simple example of how to write inference rules using Graql.
- Usage Patterns and the Economics of the Public Cloud
- Jul 6, 2017.
Research in economics and operations management posits that dynamic pricing is critically important when capacity is fixed (at least in the short run) and fixed costs represent a substantial fraction of total costs.
- Connecting with the Internet of Things
- Jul 6, 2017.
If you’re like me, you've heard a lot about the Internet of Things (IoT) but are confused about what it really is.
- Getting Started with Python for Data Analysis
- Jul 5, 2017.
A guide for beginners to Python for getting started with data analysis.
- Spotlight on the Remarkable Potential of AI in KYC (Know Your Customer)
- Jul 4, 2017.
Most people would have heard of the headline-making tremendous achievements in artificial intelligence (AI): Systems defeating world champions in board games like GO and winning quiz shows. These are small realizations of AI, but there is a silent revolution taking place in other areas, including Regulatory Compliance in Financial Services.
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 3
- Jul 4, 2017.
In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%)
- Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2
- Jul 1, 2017.
Here are deep learning examples and demos you can just download and run, including Spotify Artist Search using Speech APIs, Symbolic AI Speech Recognition, and Algorithmia API Photo Colorizer.
COVID-19 Visualized: The power of effective visualizations for pandemic storytelling
Coronavirus Data and Poll Analysis – yes, there is hope, if we act now