Deep Learning: Language identification using Keras & TensorFlow

@tachyeonz : Source: openclipart.org, license: Public Domain

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TensorFlow 101: Understanding Tensors and Graphs to get you started in Deep Learning

@tachyeonz : TensorFlow is one of the most popular libraries in Deep Learning. When I started with TensorFlow it felt like an alien language. But after attending couple of sessions in TensorFlow, I got the hang of it. I found the topic so interesting that I delved further into it.

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Optical Character Recognition Using One-Shot Learning, RNN, and TensorFlow

@tachyeonz : Optical character recognition (OCR) drives the conversion of typed, handwritten, or printed symbols into machine-encoded text. However, the OCR process brings the need to eliminate possible errors, while extracting only valuable data from ever-growing amount of it.

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GPUs are now available for Google Compute Engine and Cloud Machine Learning

@tachyeonz : The new Google Cloud GPUs are tightly integrated with Google Cloud Machine Learning (Cloud ML), helping you slash the time it takes to train machine learning models at scale using the TensorFlow framework.

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7 Simple Steps to Install TensorFlow on Windows

@tachyeonz : If I were to ask to you to describe TensorFlow in just one or two words, what would you say? Artificial Intelligence. That should sum it all nicely don’t you think so?

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3 cool machine learning projects using TensorFlow and the Raspberry Pi

@tachyeonz : 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.

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TensorFlow: How to optimise your input pipeline with queues and multi-threading

@tachyeonz : TensorFlow 1.0 is out and along with this update, some nice recommendations appeared on the TF website. One that caught my attention particularly is about the feed_dict system when you make a call to sess.run():

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Preprocessing for Machine Learning with tf.Transform

@tachyeonz : When applying machine learning to real world datasets, a lot of effort is required to preprocess data into a format suitable for standard machine learning models, such as neural networks.

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TensorFlow howto: a universal approximator inside a neural net

@tachyeonz : Today, let’s take a break from learning and implement something instead! Did you hear about the “Universal approximation theorem”?

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TensorFlow Image Recognition on a Raspberry Pi

@tachyeonz : Editor’s note: This post is part of our Trainspotting series, a deep dive into the visual and audio detection components of our Caltrain project. You can find the introduction to the series here. SVDS has previously used real-time, publicly available data to improve Caltrain arrival predictions.

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Fundamental Deep Learning code in TFLearn, Keras, Theano and TensorFlow

@tachyeonz : Yesterday evening, after years of attending the Open Statistical Programming Meetup in New York, I had the honour of giving a talk to the venerable institution on The Fundamentals of Deep Learning, replete with applications of the approach. My full slides from the evening are available here.

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Deep Learning Frameworks of 2017 Jan

@tachyeonz : Chainer is a deep learning framework that’s designed on the principle of define-by-run. Unlike frameworks that use the define-and-run approach, Chainer lets you modify networks during runtime, allowing you to use arbitrary control flow statements.

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