neural network

Image Classification


Recent advances in deep learning made tasks such as Image and speech recognition possible. Most people talk about these days whilst discussing machine learning / deep learning is Tensorflow and Neural Networks. Deep Learning is nothing but a subset of Machine Learning Algorithms which is specifically good at recognizing patterns but typically requires a large number of data. This post describes a Keras based Convolution Neural Net for image classification from scratch. There are several scripts which use pre-trained models available for image classification such as Google's Inception model.

How companies use collaborative filtering to learn exactly what you want


How do companies like Amazon and Netflix know precisely what you want? Whether it's that new set of speakers that you've been eyeballing, or the next Black Mirror episode -- their use of predictive algorithms has made the job of selling you stuff ridiculously efficient. But as much as we'd all like a juicy conspiracy theory, no, they don't employ psychics. They use something far more magical -- mathematics. Today, we'll look at an approach called collaborative filtering.

Facebook's Yann LeCunn reflects on the enduring appeal of convolutions


Thirty years ago, Yann LeCun pioneered the use of a particular form of machine learning, called the convolutional neural network, or CNN, while at the University of Toronto. That approach, moving a filter over a set of pixels to detect patterns in images, showed promise in cracking problems such as getting the computer to recognize hand-written digits with minimal human guidance. Years later, LeCun, then at NYU, launched a "conspiracy," as he has termed it, to bring machine learning back into the limelight after a long winter for the discipline. The key was LeCun's CNN, which had continued to develop in sophistication to the point where it could produce results in computer vision that stunned the field. The new breakthroughs with CNNs, along with innovations by peers such as Yoshua Bengio, of Montreal's MILA group for machine learning, and Geoffrey Hinton of Google Brain, succeeded in creating a new springtime for AI research, in the form of deep learning.

Facebook set to take on Amazon and Apple by building AI assistant with 'common sense'

Daily Mail

Facebook is serious about joining the AI assistant race. The social media giant has been expanding its effort to create its own artificial intelligence chips, Yann Lecun, Facebook's chief AI scientist, told the Financial Times. Facebook's hope is that its AI chips could one day power a voice assistant with the'common sense' to carry out a full conversation with humans and even rival voice assistants created by the likes of Amazon and Apple. Facebook is serious about joining the AI assistant race. The social media giant has been expanding its effort to create its own artificial intelligence chips, Facebook's AI lead said'In terms of new uses, one thing Facebook would be interested in is offering smart digital assistants - something that has a level of common sense,' Lecun told the FT.

Deep Learning Market 2019 Analysis and Precise Outlook- Amazon Web Services (AWS), Google, IBM, Intel, Micron Technology, Microsoft – Marketbizmail - Enterprise & Hybrid Cloud Services


The report presents an in-depth assessment of the Deep Learning including enabling technologies, key trends, market drivers, challenges, standardization, regulatory landscape, deployment models, operator case studies, opportunities, future roadmap, value chain, ecosystem player profiles and strategies. The report also presents forecasts for Deep Learning investments from 2019 till 2025. The global Deep Learning market size was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of 31.2% during 2019-2025. The report presents the market competitive landscape and a corresponding detailed analysis of the major vendor/key players in the market. For comprehensive understanding of market dynamics, the global Deep Learning Market is analysed across key geographies namely: United States, China, Europe, Japan, South-east Asia, India and others.

Deep Learning Used To Create Fake Airbnb Listings


Taking a vacation can sometimes be extremely stressful – finding the right, affordable plane tickets, packing up, getting past the airport security hassles, and most importantly, finding a place to stay. In order to escape from the mind-boggling prices and taxes, we are introduced to a website, Airbnb, which lists apartments around the world for rent. Now, a new website which uses deep learning, suggests fake Airbnb listings which might look convincing enough for people to believe they are real. How can we trust an Airbnb host and their listing? There are a plethora of photos, a detailed description and many reviews previous guests have left which ensure the apartment is the way it was described.

Derisking machine learning and artificial intelligence


The added risk brought on by the complexity of machine-learning models can be mitigated by making well-targeted modifications to existing validation frameworks. Machine learning and artificial intelligence are set to transform the banking industry, using vast amounts of data to build models that improve decision making, tailor services, and improve risk management. According to the McKinsey Global Institute, this could generate value of more than $250 billion in the banking industry.1 1.For the purposes of this article machine learning is broadly defined to include algorithms that learn from data without being explicitly programmed, including, for example, random forests, boosted decision trees, support-vector machines, deep learning, and reinforcement learning. The definition includes both supervised and unsupervised algorithms. For a full primer on the applications of artificial intelligence, we refer the reader to "An executive's guide to AI."

Evolutionary Algorithms are the New Deep Learning


Deep learning (DL) has transformed much of AI, and demonstrated how machine learning can make a difference in the real world. Its core technology is gradient descent, which has been used in neural networks since the 1980s. However, massive expansion of available training data and compute gave it a new instantiation that significantly increased its power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Importantly, however, EC addresses a different but equally far-reaching problem.

Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities


Google is getting ready to release its StreetLearn dataset for training machine-learning models to navigate cities without a map. The StreetLearn environment relies on images from Google Street View and has been used by Google DeepMind to train a software agent to navigate various western cities without reference to a map or GPS co-ordinates, using only visual clues such as landmarks as it wanders the streets. The StreetLearn environment encompasses multiple regions within the centers of the cities of London, Paris and New York. It is made up of cropped 360-degree panoramic images of street scenes from Street View, each measuring 84 x 84 pixels. Each panoramic image is a node in larger network or graph of images, with up to 65,000 nodes per 5km city region, and multiple regions per city.

Best of for AI, Machine Learning, and Deep Learning – January 2019 - insideBIGDATA


Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.