Have you played Quick, Draw! yet? It's basically Pictionary, played against a neural network, and it's a lot of fun. Not long ago, Google released a dataset of some of the millions of sketches people have drawn so far over 345 categories. In this post, I'll just be looking at sketches in the'flamingo' category. You can browse a random sample here.

In this tutorial I'll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered. A short introduction to TensorFlow is available here. For now, let's get started with the RNN! It is short for "Recurrent Neural Network", and is basically a neural network that can be used when your data is treated as a sequence, where the particular order of the data-points matter.

It has always been a debatable topic to choose between R and Python. The Machine Learning world has been divided over the preference of one language over the other. But with the explosion of Deep Learning, the balance shifted towards Python as it had an enormous list of Deep Learning libraries and frameworks which R lacked (till now). I personally switched to Python from R simply because I wanted to dive into the Deep Learning space but with an R, it was almost impossible. With launch of Keras in R, this fight is back at the center.

One of the biggest limiting factors of artificial intelligence (AI) systems is that they can't think or conceptualize the world the way humans can. Rather than intuitively discerning patterns in chaos, like how you can identify a cat in a photograph instantly, traditional AI models require in-depth descriptions of what constitutes a "cat" object and how to identify one by evaluating individual groups of pixels within the image. Deep learning systems are starting to bypass the necessity for brute force computations, as evidenced by the landmark victory of AI program AlphaGo against an international champion of Go, a game once thought to be too intuitive and conceptual for AI to master. But a new, yet intuitively simple, leap forward in AI learning may be able to accelerate the pace of AI development even further. Google researcher and AI expert Ian Goodfellow is working on AI that belongs to a group of "generative models," which are designed to create images and sounds comparable to those you'd find in the real world. This is a deceptively difficult task, as AI programs must first conceptually understand what it is they're trying to replicate, a leap forward in intuitive thinking that has historically been reserved for human beings.

Hi, so I am coming from a background in linear algebra and traditional numerical gradient-based optimization, but excited by the advancements that have been made in deep learning. To get my feet wet a bit, I made a pretty simple NN model to do some non-linear regressions for me. I uploaded my jupyter notebookit as a gist here (renders properly on github), which is pretty short and to the point. It just fits the 1D function y (x - 5)2 / 25. I know that Theano and Tensorflow are, at their core, graph based derivative (gradient) passing frameworks.

Summary: The most important developments in Deep Learning and AI in the last year may not be technical at all, but rather a major change in business model. In the space of about six months all the majors have made their Deep Learning IP open source, hoping to gain on the competition from the power of the broader developer base and wide adoption. To say that the last year has been big for Deep Learning is an understatement. There have been some spectacular technical innovations like Microsoft winning the ImageNet competition with a neural net comprised of 152 layers (where 6 or 7 layers is more the norm). But the big action especially in the last six months has been in the business model for Deep Learning.

Microsoft updates Apache Spark with new Machine Learning Library with advanced capabilities for data scientists leveraging innovation. Microsoft has unveiled a new function that caters to data scientists, with the release of a Machine Learning library for Apache Spark. The aim is to offer an increased rate of experimentation and also help data scientists leverage advanced machine and deep learning techniques on large datasets. Microsoft's Machine Learning Library (MLlib) is built to make machine learning scalable and easy to use, providing tools such as algorithms to offer classification, regression, clustering and filtering of machine learning. According to Microsoft, customers already using its SparkML have found it to be a platform which helps in building scalable machine learning models but have still struggled with low-level APIs.

Every minute spent training a deep learning model is a minute not doing something else and in today's fast paced world of research, that minute is worth a lot. Facebook published a paper this morning detailing its personal approach to this problem. The company says its managed to reduce the training time of a ResNet-50 deep learning model on ImageNet from 29 hours to one. Facebook managed to reduce training time so dramatically by distributing training in larger "minibatches" across a greater number of GPUs. In the previous benchmark case, batches of 256 images were spread across eight GPUs.

This article was posted by Adrian Rosebrock on Pyimagesearch. Adrian is an entrepreneur and Ph.D who has launched two successful image search engines, ID My Pill and Chic Engine. This list is by no means exhaustive, it's simply a list of libraries that he has used in his computer vision career and found particular useful at one time or another. The goal of this blog post is to introduce you to these libraries. He encourages you to read up on each them individually to determine which one will work best for you in your particular situation.

In May 2006, Larry page, one of Google's co-founders had said "The ultimate search engine would understand everything in the world. It would understand everything that you asked it and give you back the exact right thing instantly. You could ask'what should I ask Larry?' and it would tell you." Come 2016, it seems at least part of his vision has been achieved through the release of Tensorflow, Google's Artificial engine platform. Tensorflow is a deep learning software developed by Google as a successor to its DistBelief software, which also focused on deep learning.