Jul
2017 Aug Tutorials, Overviews
http likes 31All (113) | Courses, Education (8) | Meetings (15) | News, Features (18) | Opinions, Interviews (28) | Software (2) | Tutorials, Overviews (35) | Webcasts & Webinars (7)
- What we learned labeling 1 million images
- Aug 31, 2017.
In this guide you'll learn how to scope a computer vision project, what kind of source data you need to make it successful, what kind of tools fit your project best, and a whole lot more.
- Next Generation Data Manipulation with R and dplyr
- Aug 31, 2017.
The idea behind the dplyr package is to do one thing at a time. dplyr has separate functions for every task which make its implementation crisp and easy to understand.
- Learning Machine Learning… with Flashcards
- Aug 31, 2017.
Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.
- Using GRAKN.AI to Detect Patterns in Credit Fraud Data
- Aug 30, 2017.
The term Horn Clause Mining, similar to Rule Based Machine Learning or Inductive Logic Programming, is used to describe the inverse of this functionality. Given a large enough knowledge base, can we infer rules that describe the data accurately?
- PyTorch or TensorFlow?
- Aug 29, 2017.
PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.
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How to Become a Data Scientist: The Definitive Guide
- Aug 29, 2017.
Data science educator Jose Portilla provides this definitive guide on becoming a data scientist, which includes everything from resources for acquiring specific skills, to searching for the first job, to mastering the interview. - Support Vector Machine (SVM) Tutorial: Learning SVMs From Examples
- Aug 28, 2017.
In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.
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42 Steps to Mastering Data Science
- Aug 25, 2017.
This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond. - How To Write Better SQL Queries: The Definitive Guide – Part 2
- Aug 24, 2017.
Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.
- The Ultimate Guide to Basic Data Cleaning
- Aug 24, 2017.
Data cleaning can seem intimidating, but it’s not hard if you know the basic steps. That’s why we’re excited to announce our newest ebook, “The Ultimate Guide to Basic Data Cleaning”!
- How To Write Better SQL Queries: The Definitive Guide – Part 1
- Aug 23, 2017.
Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.
- A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets
- Aug 22, 2017.
In this blog, I explore three sets of APIs—RDDs, DataFrames, and Datasets—available in a pre-release preview of Apache Spark 2.0; why and when you should use each set; outline their performance and optimization benefits; and enumerate scenarios when to use DataFrames and Datasets instead of RDDs.
- Recommendation System Algorithms: An Overview
- Aug 22, 2017.
This post presents an overview of the main existing recommendation system algorithms, in order for data scientists to choose the best one according a business’s limitations and requirements.
- Deep Learning and Neural Networks Primer: Basic Concepts for Beginners
- Aug 18, 2017.
This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.
- A Guide to Instagramming with Python for Data Analysis
- Aug 17, 2017.
I am writing this article to show you the basics of using Instagram in a programmatic way. You can benefit from this if you want to use it in a data analysis, computer vision, or any other cool project you can think of.
- First Steps of Learning Deep Learning: Image Classification in Keras
- Aug 16, 2017.
Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
- A Guide to Understanding AI Toolkits
- Aug 16, 2017.
This post surveys today’s foremost options for AI in the form of deep learning, examining each toolkit’s primary advantages as well as their respective industry supporters.
- Comparing Distance Measurements with Python and SciPy
- Aug 15, 2017.
This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.
- Making Predictive Models Robust: Holdout vs Cross-Validation
- Aug 11, 2017.
The validation step helps you find the best parameters for your predictive model and prevent overfitting. We examine pros and cons of two popular validation strategies: the hold-out strategy and k-fold.
- Transforming from Autonomous to Smart: Reinforcement Learning Basics
- Aug 11, 2017.
This blog introduces the basics of reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice.
- Data Science Primer: Basic Concepts for Beginners
- Aug 11, 2017.
This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.
- What Is Optimization And How Does It Benefit Business?
- Aug 10, 2017.
Here we explain what Mathematical Optimisation is, and discuss how it can be applied in business and finance to make decisions.
- The Machine Learning Abstracts: Support Vector Machines
- Aug 10, 2017.
While earlier entrants in this series covered elementary classification algorithms, another (more advanced) machine learning algorithm which can be used for classification is Support Vector Machines (SVM).
- How Convolutional Neural Networks Accomplish Image Recognition?
- Aug 9, 2017.
Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.
- Google Analytics Audit Checklist and Tools
- Aug 9, 2017.
In this post, a Google Analytics & Google AdWords expert shares his tips and tools of intelligent Google Analytics auditing. Read on for some practical insight.
- Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings
- Aug 9, 2017.
This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.
- Going deeper with recurrent networks: Sequence to Bag of Words Model
- Aug 8, 2017.
Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.
- How I Used Deep Learning To Train A Chatbot To Talk Like Me
- Aug 8, 2017.
In this post, we’ll be looking at how we can use a deep learning model to train a chatbot on my past social media conversations in hope of getting the chatbot to respond to messages the way that I would.
- Insights from Data mining of Airbnb Listings
- Aug 4, 2017.
AirBnB has 2 million listings and operates in 65,000 cities. Here we look at insights related to vacation rental space in the sharing economy using the property listings data for Texas, US.
- Machine Learning Algorithms: A Concise Technical Overview – Part 1
- Aug 4, 2017.
These short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept.
- Train your Deep Learning Faster: FreezeOut
- Aug 3, 2017.
We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.
- The Machine Learning Abstracts: Decision Trees
- Aug 3, 2017.
Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree.
- Train your Deep Learning model faster and sharper: Snapshot Ensembling — M models for the cost of 1
- Aug 2, 2017.
We explain a novel Snapshot Ensembling method for increasing accuracy of Deep Learning models while also reducing training time.
- DeepSense: A unified deep learning framework for time-series mobile sensing data processing
- Aug 2, 2017.
Compared to the state-of-art, DeepSense provides an estimator with far smaller tracking error on the car tracking problem, and outperforms state-of-the-art algorithms on the HHAR and biometric user identification tasks by a large margin.
- Visualizing Convolutional Neural Networks with Open-source Picasso
- Aug 1, 2017.
Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?