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Weekly Selection – Jan 26, 2018

Handwriting recognition using Tensorflow and Keras

By Priya Dwivedi – 4 min read

Handwriting recognition aka classifying each handwritten document by its writer is a challenging problem due to huge variation in individual writing styles.


My journey into Deep Learning

By Favio Vázquez – 8 min read.

In this post I’ll share how I’ve been studying Deep Learning and using it to solve data science problems. It’s an informal post but with interesting content (I hope).


A Tour of The Top 10 Algorithms for Machine Learning Newbies

By James Le – 11 min read

In machine learning, there’s something called the "No Free Lunch" theorem. In a nutshell, it states that no one algorithm works best for every problem, and it’s especially relevant for supervised learning (i.e. predictive modeling).


-> Start Learning with Dataquest
-> Start Learning with Dataquest

Detecting Pikachu on Android using Tensorflow Object Detection

By Juan De Dios Santos – 12 min read

Deep inside the many functionalities and tools of TensorFlow, lies a component named TensorFlow Object Detection API. The purpose of this library, as the name says, is to train a neural network capable of recognizing objects in a frame, for example, an image.


Applying transfer learning in NLP and CV

By Lars Hulstaert – 8 min read

In this blog post I will discuss two applications of transfer learning. I will provide an overview of examples in the field of natural language processing and computer vision.


Uber Driver Schedule Optimization

By Ivan Zhou – 12 min read

One of Uber’s key value propositions is offering scheduling flexibility to their driver-partners. According to a report by the Beneson Strategy Group, 73% of drivers prefer having a job that lets them choose their schedule.


Being better at Machine Learning than Google – is it possible?

By Aaron Edell – 3 min read

To say you’re better at something than Google is fight’n words. However, when I ran a test of our facial recognition technology against Google’s Vision API, I discovered that we were more accurate… a lot more accurate.


Mapping The Landscape of Neuroscience(s)

By Fahd Alhazmi – 5 min read

Neuroscience is a diverse field of science made of different disciples: biology, psychology, computer science, linguistics and many more. The primary goal of brain science(s) is to understand the nervous system.


Why AI will not replace radiologists

By Hugh Harvey – 10 min read

In late 2016 Prof Geoffrey Hinton, the godfather of neural networks, said that it’s "quite obvious that we should stop training radiologists" as image perception algorithms are very soon going to be demonstrably better than humans. Radiologists are, he said, "the coyote already over the edge of the cliff who hasn’t yet looked down".


Learn By Sharing

By William Koehrsen – 4 min read

Traditional education is simple: sit down, shut up, and listen to the teacher. After class, go to the library to repeatedly read the same words, trying to figure out abstract topics with little meaning in our daily lives.


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