New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
If you're not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms' accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build. But breakthroughs in deep learning aren't the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to nonexperts, opening up access to a vast group of people.
We still need to boil the information down. In the last layer, we still want only 10 neurons for our 10 classes of digits. Traditionally, this was done by a "max-pooling" layer. Even if there are simpler ways today, "max-pooling" helps understand intuitively how convolutional networks operate: if you assume that during training, our little patches of weights evolve into filters that recognise basic shapes (horizontal and vertical lines, curves, ...) then one way of boiling useful information down is to keep through the layers the outputs where a shape was recognised with the maximum intensity. In practice, in a max-pool layer neuron outputs are processed in groups of 2x2 and only the one max one retained.
The learning rate is one of the most important hyper-parameters to tune for training deep neural networks. In this post, I'm describing a simple and powerful way to find a reasonable learning rate that I learned from fast.ai I'm taking the new version of the course in person at University of San Francisco. It's not available to the general public yet, but will be at the end of the year at course.fast.ai Deep learning models are typically trained by a stochastic gradient descent optimizer.
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users' and items' attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user's attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering.
That would allow devices to operate independent of the internet while using AI that performs almost as well as tethered neural networks. "We feel this has enormous potential," said Alexander Wong, a systems design engineering professor and Waterloo and co-creator of the technology. "This could be an enabler in many fields where people are struggling to get deep-learning AI in an operational form." The use of stand-alone deep-learning AI could lead to much lower data processing and transmission costs, greater privacy and use in areas where existing technology is impractical due to expense or other factors. Deep-learning AI, which mimics the human brain by processing data through layers and layers of artificial neurons, typically requires considerable computational power, memory and energy to function.
San Francisco, California, October 09, 2017 – The global market for deep learning is projected to undergo immense growth opportunities in the coming years, as reported by TMR Research. The report published by the market research company, titled, "Deep Learning Market – Global Industry Analysis, Size, Share, Trends, Analysis, Growth, and Forecast 2017 – 2025," explains how the growing utilization of deep learning in a few enterprises including automotive, marketing and medicinal services is the essential driver for the market. Notwithstanding that, thorough innovative work that are at present in progress are relied upon to develop the innovation and capacity of the market in a way that different enterprises can improve their product. Since deep learning systems can give master help, they help people to expand their capacities. These systems initially build up a deep space knowledge and give this data to the end-clients in an auspicious, normal, and usable way.
Intelligence agencies have a limited number of trained human analysts looking for undeclared nuclear facilities, or secret military sites, hidden among terabytes of satellite images. But the same sort of deep learning artificial intelligence that enables Google and Facebook to automatically filter images of human faces and cats could also prove invaluable in the world of spy versus spy. An early example: US researchers have trained deep learning algorithms to identify Chinese surface-to-air missile sites--hundreds of times faster than their human counterparts. The deep learning algorithms proved capable of helping people with no prior imagery analysis experience find surface-to-air missile sites scattered across nearly 90,000 square kilometers of southeastern China. Such AI based on neural networks--layers of artificial neuron capable of filtering and learning from huge amounts of data--matched the overall 90 percent accuracy of expert human imagery analysts in locating the missile sites.
I want to answer some questions that I'm commonly asked: What kind of computer do I need to do deep learning? What deep learning library do you recommend for beginners? How do you put deep learning into production? I think these questions all fall under a general theme of What do you need (in terms of hardware, software, background, and data) to do deep learning? This post is geared towards those new to the field and curious about getting started.
We're pleased to announce the next step towards deep learning for every device and platform. Today Vertex.AI is releasing PlaidML, our open source portable deep learning engine. Our mission is to make deep learning accessible to every person on every device, and we're building PlaidML to help make that a reality. We're starting by supporting the most popular hardware and software already in the hands of developers, researchers, and students. The initial version of PlaidML runs on most existing PC hardware with OpenCL-capable GPUs from NVIDIA, AMD, or Intel.
In the context of machine learning, tensor refers to the multidimensional array used in the mathematical models that describe neural networks. In other words, a tensor is usually a higher-dimension generalization of a matrix or a vector. Through a simple notation that uses a rank to show the number of dimensions, tensors allow the representation of complex n-dimensional vectors and hyper-shapes as n-dimensional arrays. Tensors have two properties: a datatype and a shape. TensorFlow is an open source deep learning framework that was released in late 2015 under the Apache 2.0 license.