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Data Scientist in the making

A curated list of articles from my continous learning journey

Top Data Scientists to Follow & Best Data Science Tutorials on GitHub

Twitter started the trend of ‘People to Follow’. This later got replicated by other platforms such as Facebook, Linkedin, Quora and GitHub. This cool feature lets you connect with the rockstars of various domains and get an access to what is going on their end without bothering them much….

Read full article at the publisher’s site: http://ift.tt/2h7ycpV

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A modern guide to getting started with Data Science and Python

Python has an extremely rich and healthy ecosystem of data science tools. Unfortunately, to outsiders this ecosystem can look like a jungle (cue snake joke). In this blog post I will provide a step-by-step guide to venturing into this PyData jungle….

Read full article at the publisher’s site: http://ift.tt/1Ad68bL

Huge Trello List of Great Data Science Resources

I have been reading and collecting data science resources for years (back in the days when BI / BA was all the rage). While there are lots of resources on the net, not all are great and some are even misleading. Now, I have updated my collection and placed them into a neat Trello list, open to all….

Read full article at the publisher’s site: http://ift.tt/2xnicM4

The future of deep learning

This post is adapted from Section 3 of Chapter 9 of my book, Deep Learning with Python (Manning Publications). It is part of a series of two posts on the current limitations of deep learning, and its future. You can read the first part here: The Limitations of Deep Learning….

Read full article at the publisher’s site: http://ift.tt/2uzcvcN

Neuroevolution: A different kind of deep learning

For a deeper dive into neuroevolution, check out Kenneth Stanley’s upcoming talk, “Evolving neural networks through neuroevolution,” at the Artificial Intelligence Conference in San Francisco, September 17-20, 2017. Early price ends August 4. Neuroevolution is making a comeback….

Read full article at the publisher’s site: http://ift.tt/2uVHy0a

Deep learning: A brief guide for practical problem solvers

Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. Because it is the most general way to model a problem, deep learning has the potential to solve the most challenging questions in machine learning and artificial intelligence….

Read full article at the publisher’s site: http://ift.tt/1HwGsfA

Building TensorFlow 1.3.0-rc2 as a standalone project (Raspberry pi 3 included)

Here you’ll learn how to build Tensorflow either for your x86_64 machine or for the raspberry pi 3 as a standalone shared library which can be interfaced from the C++ API. Watch out for the “For the Rpi” dropdown menus to know what commands are related to the Rpi and which ones aren’t….

Read full article at the publisher’s site: http://ift.tt/2hD3via

Must-read Path-breaking Papers About Image Classification

Deep Learning models for Image Classification have achieved an exponential decline in error rate through last few years. Since then, Deep Learning has become prime focus area for AI research. However, Deep Learning has been around for a few decades now….

Read full article at the publisher’s site: http://ift.tt/2vIgRNd

Multivariate Time Series Forecasting with LSTMs in Keras

Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables….

Read full article at the publisher’s site: http://ift.tt/2uDy1u1

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