2017 Dec Tutorials, Overviews
All (93) | Courses, Education (2) | Meetings (8) | News, Features (10) | Opinions, Interviews (35) | Top Stories, Tweets (9) | Tutorials, Overviews (23) | Webcasts & Webinars (6)
- How To Debug Your Approach To Data Analysis - Dec 29, 2017.
Seven common biases that influence how we understand, use, and interpret the world around us.
- 15 Minute Guide to Choose Effective Courses for Machine Learning and Data Science - Dec 28, 2017.
Advice for young professionals in non-CS field who wants to learn and contribute to data science/machine learning. Curated from personal experience.
- Simple Ways Of Working With Medium To Big Data Locally - Dec 27, 2017.
An overview of the installation and implementation of simple techniques for working with large datasets in your machine.
- SQL Window Functions Tutorial for Business Analysis - Dec 27, 2017.
In this SQL window functions tutorial, we will describe how these functions work in general, what is behind their syntax, and show how to answer these questions with pure SQL.
- An Introduction to Monte Carlo Tree Search - Dec 22, 2017.
A great explanation of the concept behind Monte Carlo Tree Search algorithm and a brief example of how it was used at the European Space Agency for planning interplanetary flights.
-
Computer Vision by Andrew Ng - 11 Lessons Learned - Dec 22, 2017.
I recently completed Andrew Ng’s computer vision course on Coursera. In this article, I will discuss 11 key lessons that I learned in the course. - Deep Learning Made Easy with Deep Cognition - Dec 21, 2017.
So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.
- Getting Started with TensorFlow: A Machine Learning Tutorial - Dec 19, 2017.
A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.
- A Guide for Customer Retention Analysis with SQL - Dec 19, 2017.
Customer retention curves are essential to any business looking to understand its clients, and will go a long way towards explaining other things like sales figures or the impact of marketing initiatives. They are an easy way to visualize a key interaction between customers and the business.
- NIPS 2017 Key Points & Summary Notes - Dec 18, 2017.
Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.
- Best Masters in Data Science and Analytics – Asia and Australia Edition - Dec 14, 2017.
The fourth edition of our comprehensive, unbiased survey on graduate degrees in Data Science and Analytics from around the world.
- How to Generate FiveThirtyEight Graphs in Python - Dec 14, 2017.
In this post, we'll help you. Using Python's matplotlib and pandas, we'll see that it's rather easy to replicate the core parts of any FiveThirtyEight (FTE) visualization.
- The 10 Deep Learning Methods AI Practitioners Need to Apply - Dec 13, 2017.
Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.
- Today I Built a Neural Network During My Lunch Break with Keras - Dec 8, 2017.
So yesterday someone told me you can build a (deep) neural network in 15 minutes in Keras. Of course, I didn’t believe that at all. So the next day I set out to play with Keras on my own data.
- Best Masters in Data Science and Analytics – Europe Edition - Dec 7, 2017.
The third part of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics, examining the programs from Europe.
- Managing Machine Learning Workflows with Scikit-learn Pipelines Part 1: A Gentle Introduction - Dec 7, 2017.
Scikit-learn's Pipeline class is designed as a manageable way to apply a series of data transformations followed by the application of an estimator.
- Some Musings on Capsule Networks and DLPaper2Code - Dec 6, 2017.
Only the Godfather of Deep Learning did it again and came up with something brilliant — adding layers inside existing layers instead of adding more layers i.e nested layers.... giving rise to the Capsule Networks!
- Multi-objective Optimization for Feature Selection - Dec 5, 2017.
By having the model analyze the important signals, we can focus on the right set of attributes for optimization. As a side effect, less attributes also mean that you can train your models faster, making them less complex and easier to understand.
- Big Data: Main Developments in 2017 and Key Trends in 2018 - Dec 5, 2017.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.
- What is a Bayesian Neural Network? - Dec 5, 2017.
BNNs are important in specific settings, especially when we care about uncertainty very much.
- Graph Analytics Using Big Data - Dec 4, 2017.
An overview and a small tutorial showing how to analyze a dataset using Apache Spark, graphframes, and Java.
- Exploring Recurrent Neural Networks - Dec 1, 2017.
We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.
- A General Approach to Preprocessing Text Data - Dec 1, 2017.
Recently we had a look at a framework for textual data science tasks in their totality. Now we focus on putting together a generalized approach to attacking text data preprocessing, regardless of the specific textual data science task you have in mind.
Computer Vision by Andrew Ng - 11 Lessons Learned
Big Data: Main Developments in 2017 and Key Trends in 2018
Nov
