TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium

#TensorFlow or #Keras? Which one should I learn?

  • With plenty of libraries out there for deep learning, one thing that confuses a beginner in this field the most is which library to choose.Deep Learning libraries/frameworks as per popularity(Source : Google)In this blog post, I am only going to focus on Tensorflow and Keras.
  • And if Keras is more user-friendly, why should I ever use TF for building deep learning models?
  • You can tweak TF much more as compared to Keras.FunctionalityAlthough Keras provides all the general purpose functionalities for building Deep learning models, it doesn’t provide as much as TF.
  • Absolutely, check the example below:Playing with gradients in TensorFlow (Credits : CS 20SI: TensorFlow for Deep Learning Research)Conclusion (TL;DR)if you are not doing some research purpose work or developing some special kind of neural network, then go for Keras (trust me, I am a Keras fan!!)
  • But as we all know that Keras is going to be integrated in TF, it is wiser to build your network using tf.contrib.Keras and insert anything you want in the network using pure TensorFlow.

Deep learning is everywhere. 2016 was the year where we saw some huge advancements in the field of Deep Learning and 2017 is all set to see many more advanced use cases. With plenty of libraries out…
Continue reading “TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium”

Artificial intelligence now powers all of Facebook’s translation

Artificial intelligence now powers all of Facebook’s translation

  • On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
  • Back in May, the company’s artificial intelligence division, called Facebook AI Research, announced that they had developed a kind of neural network called a CNN (that stands for convolutional neural network, not the news organization where Wolf Blitzer works) that was a fast, accurate translator.
  • Now, Facebook says that they have incorporated that CNN tech into their translation system, as well as another type of neural network, called an RNN (the R is for recurrent).
  • Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a “phrase-based machine translation” technique that wasn’t powered by neural networks.
  • As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it.

On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
Continue reading “Artificial intelligence now powers all of Facebook’s translation”

Real-time Performance RNN in the Browser

Lovely music: listen to a real-time performance RNN in the browser with deeplearn.js:

  • Thanks to the recently-released deeplearn.js environment, you can now enjoy real-time Performance RNN piano performances in the browser.
  • deeplearn.js is an open-source Javascript library that enables GPU-based training and evaluation of models in the browser.
  • It includes tools for porting TensorFlow models, which we were able to use to translate a Performance RNN checkpoint into a format that deeplearn.js could use.
  • We then translated our model architecture to use the deeplearn.js TypeScript libraries and added a user interface.
  • We hope you enjoy playing with Performance RNN in the browser and that this serves as an example of how easy it is to translate TensorFlow models to the web.

Thanks to the recently-released deeplearn.js environment, you can now enjoy real-time Performance RNN piano performances in the browser. …
Continue reading “Real-time Performance RNN in the Browser”

The World’s First Album Composed and Produced by an AI Has Been Unveiled

World's first album written by a robot or #AI unveiled

  • In Brief A music album called I AM AI, the featured single of which is set to release on August 21st, is the first album that’s entirely composed and produced by an artificial intelligence.
  • The AI was developed by a team of professional musicians and technology experts, and it’s the the very first AI to compose and produced an entire music album.
  • Check out the song “Break Free” in the video below:

    As film composer Drew Silverstein, one of Amper’s founders, explained to TechCrunch, Amper isn’t meant to act totally on its own, but was designed specifically to work in collaboration with human musicians: “One of our core beliefs as a company is that the future of music is going to be created in the collaboration between humans and AI.

  • That said, the team notes that, contrary to the other songs that have been released by AI composers, the chord structures and instrumentation of “Break Free” are entirely the work of Amper’s AI.
  • Yet, while IAMAI may be the first album that’s entirely composed and produced by an AI, it’s not the first time an AI has displayed creativity in music or in other arts.

A music album called IAMAI, which is set to release on August 21st, is the first that’s entirely composed by an artificial intelligence.
Continue reading “The World’s First Album Composed and Produced by an AI Has Been Unveiled”

Building Convolutional Neural Networks with Tensorflow

Building Convolutional Neural Networks with #Tensorflow #abdsc

  • In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow.
  • Later on we can use this knowledge as a building block to make interesting Deep Learning applications.
  • Source code is also provided.
  • Before you continue, make sure you understand how a convolutional neural network works.
  • The code is also available in my GitHub repository, so feel free to use it on your own dataset(s).

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from…
Continue reading “Building Convolutional Neural Networks with Tensorflow”

Artificial intelligence now powers all of Facebook’s translation

Artificial intelligence now powers all of Facebook’s translation

  • On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
  • Back in May, the company’s artificial intelligence division, called Facebook AI Research, announced that they had developed a kind of neural network called a CNN (that stands for convolutional neural network, not the news organization where Wolf Blitzer works) that was a fast, accurate translator.
  • Now, Facebook says that they have incorporated that CNN tech into their translation system, as well as another type of neural network, called an RNN (the R is for recurrent).
  • Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a “phrase-based machine translation” technique that wasn’t powered by neural networks.
  • As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it.

On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
Continue reading “Artificial intelligence now powers all of Facebook’s translation”

Receptive Field Calculator

We built a tool for calculating the receptive field of convolutional filters:  #deeplearning

  • A convolutional layer operates over a local region of the input to that layer with the size of this local region usually specified directly.
  • You can also compute the effective receptive field of a convolutional layer which is the size of the input region to the network that contributes to a layers’ activations.
  • For example, if the first convolutional layer has a receptive field of 3×3 then it’s effective receptive field is also 3×3 since it operates directly on the input.
  • However if the second layer of a convolutional network also has a 3×3 filter, then it’s (local) receptive field is 3×3, but it’s effective receptive field is 5×5.

A convolutional layer operates over a local region of the input to that layer with the size of this local region usually specified directly. You can also compute the effective receptive field of a convolutional layer which is the size of the input region to the network that contributes to a layers’ activations. For example, if the first convolutional layer has a receptive field of 3×3 then it’s effective receptive field is also 3×3 since it operates directly on the input.
Continue reading “Receptive Field Calculator”

Google is using machine learning to sort good apps from bad on the Play Store

Google is using machine learning to sort good apps from bad on the Play Store

  • Google is using machine learning to group apps by function and spot the bad apples.
  • Image: Google

    With machine learning, Google can use peer grouping to scan apps that are being loaded on to the Play Store en masse.

  • “We focus on signals that can negatively affect user privacy, such as permission requests that are not related to core app functionality, and the actual, observed behaviors,” explains Martin Pelikan of Google’s security and privacy team over email.
  • In its most recent annual Android security review, the percentage of users who had installed harmful apps from the official Play Store fell from 0.15 percent in 2015 to 0.05 percent in 2016.
  • Many users — particularly those in China — install Android apps from alternative app stores, which the company doesn’t have control over.

Security on Android has always been a challenge for Google due to the operating system’s open nature. But in recent years, the company has been gaining ground in its fight against malware and…
Continue reading “Google is using machine learning to sort good apps from bad on the Play Store”

Learning Deep Learning with Keras

Learning #DeepLearning with #Keras  #NeuralNetworks @pmigdal

  • For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python.
  • Deep learning is a name for machine learning techniques using many-layered artificial neural networks.
  • See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):

    In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.

  • Deep learning (that is – neural networks with many layers) uses mostly very simple mathematical operations – just many of them.
  • Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet.

I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi…
Continue reading “Learning Deep Learning with Keras”

GitHub

Our implementation of graph auto-encoders (in TensorFlow) is now available on GitHub:

  • This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper:

    Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs.

  • GAEs have successfully been used for:

    GAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs:

    A high-level introduction is given in our blog post:

    In order to use your own data, you have to provide

    Have a look at the function in for an example.

  • In this example, we load citation network data (Cora, Citeseer or Pubmed).
  • The original datasets can be found here: and here (in a different format): can specify a dataset as follows:

    You can choose between the following models:

    Please cite our paper if you use this code in your own work:

gae – Implementation of Graph Auto-Encoders in TensorFlow
Continue reading “GitHub”