[unable to retrieve full-text content]We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.
Original Post: Ranking Popular Deep Learning Libraries for Data Science
[unable to retrieve full-text content]This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.
Original Post: TensorFlow: Building Feed-Forward Neural Networks Step-by-Step
[unable to retrieve full-text content]We need a greater emphasis on the Systems Engineering aspects of Data Science. I am exploring these ideas as part of my course “Data Science for Internet of Things” at the University of Oxford.
Original Post: Data Science –The need for a Systems Engineering approach
[unable to retrieve full-text content]Also Jupyter Notebooks are Breathtakingly Featureless – Use Jupyter Lab; The 4 Types of Data #Analytics; Aspiring Data Scientists! Learn the basics with these 7 books.
Original Post: Top KDnuggets tweets, Sep 27 – Oct 03: Introduction to #Blockchains & What It Means to #BigData; 7 More Steps to Mastering #MachineLearning With #Python
[unable to retrieve full-text content]At the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.
Original Post: Neural Networks: Innumerable Architectures, One Fundamental Idea
[unable to retrieve full-text content]This conference brings together a range of expert practitioners to explore and discuss the new era of AI, Machine Learning and Deep Learning. Participants gain real insights on how to exploit these technological advances for themselves and their organisations in an increasingly ‘data-driven world’.
Original Post: Data Science, AI & Deep Learning Conference – 16 November 2017, London
[unable to retrieve full-text content]This blog explores how the massive parallel processing power of the GPU is able to unify the entire AI pipeline on a single platform, and how this is both necessary and sufficient for overcoming the challenges to operationalizing AI.
Original Post: GPU-accelerated, In-database Analytics for Operationalizing AI
[unable to retrieve full-text content]This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.
Original Post: Tensorflow Tutorial, Part 2 – Getting Started
[unable to retrieve full-text content]Also: Older news, but still inspiring: #Harvard Thinks It Found the Next #Einstein; Putting #MachineLearning in Production – how to guide.
Original Post: Top KDnuggets tweets, Sep 20-26: 30 Essential #DataScience, #MachineLearning & #DeepLearning Cheat Sheets