Data are becoming the new raw material of business
The Economist


Tensorflow with Keras – Empowering Neural Networks for Deep Learning

Building deep neural networks just got easier. TensorFlow has announced that they are incorporating the popular deep learning API, Keras, as part of the core code that ships with TensorFlow 1.2. In the words of Keras’ author François Chollet, “Theano and TensorFlow are closer to NumPy, while Keras is closer to scikit-learn,” which is to say that Keras is at a higher level compared to pure TensorFlow and makes building deep learning models much more manageable.

TensorFlow is one of the fastest, most flexible, and most scalable machine-learning libraries available. It was developed internally by Google Brain and released as an open-source library in November 2015. Almost immediately upon its release, TensorFlow became one of the most popular machine learning libraries. But, as is the case with many libraries that emphasize speed and flexibility, TensorFlow tends to be a bit low-level.

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Ranking Popular Deep Learning Libraries for Data Science

Gold Blog
At The Data Incubator, we pride ourselves on having the most up to date data science curriculum available. Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Here are the results.
 

The Rankings

Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average = score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average. See below for methods.


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The Many Facets of Artificial Intelligence

artificial-intelligence-2228610_960_720When you think of artificial intelligence (AI), do you envision C-3PO or matrix multiplication? HAL 9000 or pruning decision trees? This is an example of ambiguous language, and for a field which has gained so much traction in recent years, it’s particularly important that we think about and define what we mean by artificial intelligence – especially when communicating between managers, salespeople, and the technical side of things. These days, AI is often used as a synonym for deep learning, perhaps because both ideas entered popular tech-consciousness at the same time. In this article I’ll go over the big picture definition of AI and how it differs from machine learning and deep learning. Continue reading