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How to Develop an Information Maximizing GAN (InfoGAN) in Keras

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Taken from the InfoGan paper. Let's start off by developing the generator model as a deep convolutional neural network (e.g. a DCGAN). The model could take the noise vector (z) and control vector (c) as separate inputs and concatenate them before using them as the basis for generating the image. Alternately, the vectors can be concatenated beforehand and provided to a single input layer in the model. The approaches are equivalent and we will use the latter in this case to keep the model simple.


AI and understanding semantics, next stage in evolution of NLP is close

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AI is a misnomer, or so it is often suggested. The first letter -- artificial -- is about right. As for the second word -- well, there is nothing intelligent about it. Take semantics as an example, there is nothing remotely intelligent, or otherwise, about artificial technology understanding the meaning in sentences, paragraphs and books for the simple reason, it is unremittingly bad at it. But could this be about to change?


Mathematics for Artificial Intelligence – Linear Algebra

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Machine Learning, Neural Networks and Artificial intelligence are big buzzwords of the decade. It is not surprising that today these fields are expanding pretty quickly and are used to solve a vast amount of problems. We are witnesses of the new golden period of these technologies. However, today we are merely innovating. Majority of the concepts used in these fields were invented 50 or more years ago.


Aroma: Using ML for code recommendation

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Thousands of engineers write the code to create our apps, which serve billions of people worldwide. This is no trivial task--our services have grown so diverse and complex that the codebase contains millions of lines of code that intersect with a wide variety of different systems, from messaging to image rendering. To simplify and speed the process of writing code that will make an impact on so many systems, engineers often want a way to find how someone else has handled a similar task. We created Aroma, a code-to-code search and recommendation tool that uses machine learning (ML) to make the process of gaining insights from big codebases much easier. Prior to Aroma, none of the existing tools fully addressed this problem.


Having Fun with Self-Organizing Maps

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Self-Organizing Maps (SOM), or Kohonen Networks ([1]), is an unsupervised learning method that can be applied to a wide range of problems such as: data visualization, dimensionality reduction or clustering. It was introduced in the 80' by computer scientist Teuvo Kohonen as a type of neural network ([Kohonen 82],[Kohonen 90]). In this post we are going to present the basics of the SOM model and build a minimal python implementation based on numpy. There is a huge litterature on SOMs (see [2]), theoretical and applied, this post only aims at having fun with this model over a tiny implementation. The approach is very much inspired by this post ([3]).


Using Zipf's Law To Improve Neural Language Models - ___ - Medium

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In this article, I will explain what is Zipf's Law in the context of Natural Language Processing (NLP) and how knowledge of this distribution has been used to build better neural language models. I assume the reader is familiar with the concept of neural language models. The code to reproduce the numbers and figures presented in this article can downloaded from this repository. The analysis discussed in this article is based on the raw wikitext-103 corpus. This corpus consist of articles extracted from the set of Good and Featured wikipedia articles and has over 100 million tokens.


Autoencoders: Deep Learning with TensorFlow's Eager Execution

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Deep Learning has revolutionized the Machine Learning scene in the last years. Can we apply it to image compression? How well can a Deep Learning algorithm reconstruct pictures of kittens? Today we'll find the answers to all of those questions. I've talked about Unsupervised Learning before: applying Machine Learning to discover patterns in unlabelled data.


With little training, machine-learning algorithms can uncover hidden scientific knowledge

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Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials. "Without telling it anything about materials science, it learned concepts like the periodic table and the crystal structure of metals," said Jain. "That hinted at the potential of the technique. But probably the most interesting thing we figured out is, you can use this algorithm to address gaps in materials research, things that people should study but haven't studied so far."


Backpropagation for people who are afraid of math

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Backpropagation is one of the most important concepts in machine learning. There are many online resources that explain the intuition behind this algorithm (IMO the best of these is the backpropagation lecture in the Stanford cs231n video lectures. Another very good source, is this), but getting from the intuition to practice, can be (put gently) quite challenging. After spending more hours then i'd like to admit, trying to get all the sizes of my layers and weights to fit, constantly forgetting what's what, and what's connected where, I sat down and drew some diagrams that illustrates the entire process. Consider it a visual pseudocode.


Text Mining of Scientific Literature Can Lead to New Discoveries

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Berkeley Lab researchers (from left) Vahe Tshitoyan, Anubhav Jain, Leigh Weston, and John Dagdelen used machine learning to analyze 3.3 million abstracts from materials science papers. Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials. "Without telling it anything about materials science, it learned concepts like the periodic table and the crystal structure of metals," says Jain. "That hinted at the potential of the technique. But probably the most interesting thing we figured out is, you can use this algorithm to address gaps in materials research, things that people should study but haven't studied so far."