Tensorflow Tutorial : Part 2 – Getting Started

Tensorflow Tutorial : Part 2 – Getting Started

  • This post is the second part of the multi-part series on a complete tensorflow tutorial – – – If you have tensorflow already installed, you can just skip to the next section.
  • Below we have the different data types in supported by Tensorflow.
  • Note: Quantitized values [qint8, qint16 and quint8] are special values for tensorflow that help reduce the size of the data.
  • In fact, Google has gone to the extent of introducing Tensorflow Processing Units (TPUs) to speed up computation by leveraging quantitized values – – We will quickly generate some data to get started.
  • In the next part, we will finally be ready to train our first tensorflow model on house prices.

In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that…
Continue reading “Tensorflow Tutorial : Part 2 – Getting Started”

Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center

Researchers from @NVIDIA used #GANs to generate photorealistic images of fake celebrities.

  • Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
  • Rather than train a single neural network to recognize pictures, researchers train two competing networks.
  • “The key idea is to grow both the generator and discriminator progressively:  starting from a low resolution, we add new layers that model increasingly fine details as training progresses,” explained the researchers in their paper Progressive Growing of GANs for Improved Quality, Stability and Variation.
  • Since the publicly available CelebFaces Attributes (CelebA) training dataset varied in resolution and visual quality — and not sufficient enough for high output resolution — the researchers generated a higher-quality version of the dataset consisting of 30,000 images at 1024 x 1024 resolution.
  • Generating convincing realistic images with GANs are within reach and the researchers plan to use TensorFlow and multi-GPUs for the next part of the work.

Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
Continue reading “Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center”

Vertex.AI

Vertex.AI - Announcing PlaidML: Open Source #DeepLearning for Every Platform

  • Our company uses PlaidML at the core of our deep learning vision systems for embedded devices, and to date we’ve focused on support for image processing neural networks like ResNet-50, Xception, and MobileNet.
  • We wrote about this in a previous post comparing PlaidML inference throughput to TensorFlow on cuDNN.
  • After updating to Keras 2.0.8, cuDNN 6, and Tensorflow 1.3, it’s within about 4% of PlaidML’s throughput: – – It’s a great improvement and we continue to use TensorFlow as our benchmark for other areas where PlaidML is less mature.
  • Briefly, the system requirements are: – – To get PlaidML installed and do a quick benchmark all you need to do is: – – By default, plaidbench will benchmark 1024 inferences at batch size 1 using Keras on PlaidML and print a result similar to the following: – – In…
  • Then run plaidbench with the “no-plaid” option: – – The output should look like the following: – – PlaidML can take longer to execute on the first run, but tends to outperform TensorFlow + cuDNN, even on the latest NVIDIA hardware (in this case by about 14%).

We’re pleased to announce the next step towards deep learning for every device and platform. Today Vertex.AI is releasing PlaidML, our open source portable deep learning engine. Our mission is make deep learning accessible to every person on every device, and we’re building PlaidML to help make that a reality. We’re starting by supporting the most popular hardware and software already in the hands of developers, researchers, and students. The initial version of PlaidML runs on most existing PC hardware with OpenCL-capable GPUs from NVIDIA, AMD, or Intel. Additionally, we’re including support for running the widely popular Keras framework on top of Plaid to allow existing code and tutorials to run unchanged.
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An Introduction to Implementing Neural Networks using TensorFlow

An Introduction to Implementing Neural Networks using TensorFlow

  • This article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh.
  • 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.
  • 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.
  • After reading this article you will be able to understand application of neural networks and use TensorFlow to solve a real life problem.

This article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh. Faizan is a Data Science enthusiast and a Deep l…
Continue reading “An Introduction to Implementing Neural Networks using TensorFlow”

An Introduction to Implementing Neural Networks using TensorFlow

An Introduction to Implementing Neural Networks using TensorFlow

  • This article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh.
  • 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.
  • 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.
  • After reading this article you will be able to understand application of neural networks and use TensorFlow to solve a real life problem.

This article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh. Faizan is a Data Science enthusiast and a Deep l…
Continue reading “An Introduction to Implementing Neural Networks using TensorFlow”

Tensorflow Tutorial : Part 2 – Getting Started

Tensorflow Tutorial : Part 2 – Getting Started #abdsc

  • This post is the second part of the multi-part series on a complete tensorflow tutorial – – – If you have tensorflow already installed, you can just skip to the next section.
  • Below we have the different data types in supported by Tensorflow.
  • Note: Quantitized values [qint8, qint16 and quint8] are special values for tensorflow that help reduce the size of the data.
  • In fact, Google has gone to the extent of introducing Tensorflow Processing Units (TPUs) to speed up computation by leveraging quantitized values – – We will quickly generate some data to get started.
  • In the next part, we will finally be ready to train our first tensorflow model on house prices.

In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that…
Continue reading “Tensorflow Tutorial : Part 2 – Getting Started”

An Introduction to Implementing Neural Networks using TensorFlow

An Introduction to Implementing Neural Networks using TensorFlow

  • This article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh.
  • 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.
  • 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.
  • After reading this article you will be able to understand application of neural networks and use TensorFlow to solve a real life problem.

This article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh. Faizan is a Data Science enthusiast and a Deep l…
Continue reading “An Introduction to Implementing Neural Networks using TensorFlow”