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An Introduction to Implementing Neural Networks using TensorFlow

#artificialintelligence

If you have been following Data Science / Machine Learning, you just can't miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can. From running competitions to open sourcing projects and paying big bonuses, people are trying every possible thing to tap into this limited pool of talent. Self driving engineers are being hunted by the big guns in automobile industry, as the industry stands on the brink of biggest disruption it faced in last few decades! If you are excited by the prospects deep learning has to offer, but have not started your journey yet – I am here to enable it.


An Introduction to Implementing Neural Networks using TensorFlow

#artificialintelligence

Starting with this article, I will write a series of articles on deep learning covering the popular Deep Learning libraries and their hands-on implementation. Fast forward to 2012, a deep neural network architecture won the ImageNet challenge, a prestigious challenge to recognise objects from natural scenes. Nodes in the graph represents mathematical operations, while graph edges represent multi-dimensional data arrays (aka tensors) communicated between them. Here we solve our deep learning practice problem – Identify the Digits.


An Introduction to Implementing Neural Networks using TensorFlow

#artificialintelligence

If you have been following Data Science / Machine Learning, you just can't miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can. From running competitions to open sourcing projects and paying big bonuses, people are trying every possible thing to tap into this limited pool of talent. Self driving engineers are being hunted by the big guns in automobile industry, as the industry stands on the brink of biggest disruption it faced in last few decades! If you are excited by the prospects deep learning has to offer, but have not started your journey yet – I am here to enable it.


Using the TensorFlow API: An Introductory Tutorial Series

@machinelearnbot

Editor's note: The TensorFlow API has undergone changes since this series was first published. However, the general ideas are the same, and an otherwise well-structured tutorial such as this provides a great jumping off point and opportunity to consult the API documentation to identify and implement said changes. In this tutorial I'll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered. A short introduction to TensorFlow is available here.


3 cool machine learning projects using TensorFlow and the Raspberry Pi

@machinelearnbot

In early 2017, the Raspberry Pi Foundation announced a Google developer survey, which requested feedback from the maker community on what tools they wanted on the Raspberry Pi. The blog post says that Google has developed tools for machine learning, IoT, wearables, robotics, and home automation, and that the survey mentions face- and emotion-recognition, speech-to-text translation, natural language processing, and sentiment analysis. "The survey will help them get a feel for the Raspberry Pi community, but it'll also help us get the kinds of services we need," the post explains. Meanwhile, data scientists aren't waiting around to put Google's TensorFlow, an open source software library for machine learning, to work on the Raspberry Pi.