2017 Jun Tutorials, Overviewshttp likes 455
All (100) | Courses, Education (10) | Meetings (11) | News, Features (13) | Opinions, Interviews (26) | Software (6) | Tutorials, Overviews (31) | Webcasts & Webinars (3)
- Applying Deep Learning to Real-world Problems - Jun 30, 2017.
In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.
- Web Scraping with R: Online Food Blogs Example - Jun 29, 2017.
We consider scraping data from online food blogs to construct a data set of recipes with ingredients, nutritional information and more, and do exploratory analysis which provides tasty insights.
- Using the TensorFlow API: An Introductory Tutorial Series - Jun 28, 2017.
This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 2 - Jun 27, 2017.
In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles.
- Deep Learning with R + Keras - Jun 27, 2017.
Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple’s CoreML, and Theano. It is becoming the de factor language for deep learning.
- Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners - Jun 26, 2017.
Here are deep learning demos and examples you can just download and run. No Math. No Theory. No Books.
- Taxonomy of Methods for Deep Meta Learning - Jun 22, 2017.
This post discusses a variety of contemporary Deep Meta Learning methods, in which meta-data is manipulated to generate simulated architectures. Current meta-learning capabilities involve either support for search for architectures or networks inside networks.
- Golden State Warriors Analytics Exercise - Jun 22, 2017.
This post outlines a data analysis exercise undertaken by students in a recent University of San Francisco MBA class, in which they were forced to make difficult data science trade-offs between gathering data, preparing the data and performing the actual analysis.
- Making Sense of Machine Learning - Jun 21, 2017.
Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data.
- Does Machine Learning Have a Future Role in Cyber Security? - Jun 20, 2017.
In the past, ML learning hasn't had as much success in cyber security as in other fields. Many early attempts struggled with problems such as generating too many false positives, which resulted mixed attitudes towards it.
- 75 Big Data Terms to Know to Make your Dad Proud - Jun 19, 2017.
Here is a good list of 75 Big Data terms you can use to impress your father, even if you already bought him a gift.
- The Machine Learning Algorithms Used in Self-Driving Cars - Jun 19, 2017.
Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.
- K-means Clustering with Tableau – Call Detail Records Example - Jun 16, 2017.
We show how to use Tableau 10 clustering feature to create statistically-based segments that provide insights about similarities in different groups and performance of the groups when compared to each other.
- Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017.
What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.
- The Surprising Complexity of Randomness - Jun 15, 2017.
The reason we have pseudorandom numbers is because generating true random numbers using a computer is difficult. Computers, by design, are excellent at taking a set of instructions and carrying them out in the exact same way, every single time.
- Medical Image Analysis with Deep Learning , Part 3 - Jun 15, 2017.
In this article we will focus — basic deep learning using Keras and Theano. We will do 2 examples one using keras for basic predictive analytics and other a simple example of image analysis using VGG.
- Open Innovation and Crowdsourcing in Machine Learning – Getting premium value out of data - Jun 14, 2017.
Recently, PSL Research University launched a one-week course combining theoretical lectures and practical sessions. 115 students from various backgrounds and skill levels were enrolled; something quite spectacular happened during the week: Students have achieved an astounding level of score improvement - in just three afternoons.
- Top 15 Python Libraries for Data Science in 2017 - Jun 13, 2017.
Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.
- Deep Learning Papers Reading Roadmap - Jun 13, 2017.
The roadmap is constructed in accordance with the following four guidelines: from outline to detail; from old to state-of-the-art; from generic to specific areas; focus on state-of-the-art.
- Deep Learning: TensorFlow Programming via XML and PMML - Jun 9, 2017.
In this approach, problem dataset and its Neural network are specified in a PMML like XML file. Then it is used to populate the TensorFlow graph, which, in turn run to get the results.
- A Practical Guide to Machine Learning: Understand, Differentiate, and Apply - Jun 9, 2017.
So, if Machine Learning was first defined in 1959, why is this now the time to seize the opportunity? It’s the economics.
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part I - Jun 8, 2017.
As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!
- Machine Learning in Real Life: Tales from the Trenches to the Cloud – Part 1 - Jun 8, 2017.
We live in a world where everyone knows enough about the Buzzwords “Deep Learning” and “Big Data”... we also live in a world where if you’re a developer you can, while knowing nothing about machine learning, go from zero to training a OCR model in the space of an hour.
- How HR Managers Use Data Science to Manage Talent for Their Companies - Jun 7, 2017.
Data sciences can also be used by HR manager to create several estimates like the investment on talent pool, cost per hire, cost on training, and cost per employee. It provides better techniques for optimization, forecasting, and reporting.
- Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering - Jun 7, 2017.
The second post in this series of tutorials for implementing machine learning workflows in Python from scratch covers implementing the k-means clustering algorithm.
- 6 Interesting Things You Can Do with Python on Facebook Data - Jun 6, 2017.
Facebook has a huge amount of data that is available for you to explore, you can do many things with this data. I will be sharing my experience with you on how you can use the Facebook Graph API for analysis with Python.
- K-means Clustering with R: Call Detail Record Analysis - Jun 6, 2017.
Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics.
- Deep Learning 101: Demystifying Tensors - Jun 2, 2017.
Many deep-learning systems available today are based on tensor algebra, but tensor algebra isn’t tied to deep-learning. It isn’t hard to get started with tensor abuse but can be hard to stop.
- 7 Steps to Mastering Data Preparation with Python - Jun 2, 2017.
Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.
- Which Machine Learning Algorithm Should I Use? - Jun 1, 2017.
A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?” The answer to the question varies depending on many factors, including the size, quality, and nature of data, the available computational time, and more.
- 7 Techniques to Handle Imbalanced Data - Jun 1, 2017.
This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.