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”

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”

The Self-Driving Project That Could Help China Leapfrog the West

The #Chinese plan to overtake all #selfdriving cars #baidu #AI

  • One of Baidu’s experimental self-driving

    The CEO of Baidu, Robin Li, arrived at his company’s first AI developer conference, held in Beijing this week, in a vehicle that has the potential to reshape the world of self-driving cars.

  • The vehicle was controlled using software that Baidu (50 Smartest Companies 2017) plans to offer for free in the coming years through a project called Apollo.
  • The Apollo platform consists of a core software stack, a number of cloud services, and self-driving vehicle hardware such as GPS, cameras, lidar, and radar.
  • Dawen Zhou, Apollo’s principle product manager, explained that the simulation platform being developed by Baidu would be used to test the code and also to train self-driving algorithms.
  • Since lots of real-world driving data is vital for the continued improvement for Baidu’s self-driving technology, one of the biggest benefits of opening up the Apollo platform is the data Baidu can receive from its partners.

Baidu opens up its software, a stark departure from the normally secretive world of commercial AI development.
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Practical Deep Learning For Coders—18 hours of lessons for free

Practical #DeepLearning For Coders—18 hours of lessons for free

  • After this course, I cannot ignore the new developments in deep learning—I will devote one third of my machine learning course to the subject.
  • I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
  • Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms.
  • But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

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You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

  • This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and Victor Lempitsky, that uses deep architecture to move human eyeballs in a still image.
  • The authors of the demo acknowledge that similar projects already exist (like the smile-manipulator FaceApp), but without such a singular, detailed focus.
  • I tried this using images of Keanu Reeves and several dogs, but the demo didn’t work with the dogs.
  • “Our system is reasonably robust against different head poses and deals correctly with the situations where a person wears glasses,” the authors wrote in their study.
  • The authors say they plan to work on making the demo work more quickly in the future.

Another day, another fun internet thing that uses neural networks for facial manipulation. This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and…
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6 areas of AI and Machine Learning to watch closely

#ICYMI 6 areas of #AI and #MachineLearning to watch closely

  • Those who are able to train faster and deploy AI models that are computationally and energy efficient are at a significant advantage.
  • Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”.
  • If we want AI systems to solve tasks where training data is particularly challenging, costly, sensitive, or time-consuming to procure, it’s important to develop models that can learn optimal solutions from less examples (i.e. one or zero-shot learning).
  • Deep learning models are notable for requiring enormous amounts of training data to reach state-of-the-art performance.
  • Without large scale training data, deep learning models won’t converge on their optimal settings and won’t perform well on complex tasks such as speech recognition or machine translation.


Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

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