It could be a revolution in Deep Learning. Artificial Neural Networks rely on layers. One might argue that the more layers there are, the more accurate the model is. To capture the most information, each layer will represent a type of feature (from a geometrical shape to a specific concept such as a species). But what if we replaced all the layers with calculus equations, such that it would act as an infinite layers network?

By the end of this year, it's predicted that Deep Learning will be a core component in the tool-kit of 80 per cent of data scientists. That's a pretty big jump considering machine learning and AI are still considered to be cutting edge technologies and Deep Learning goes even further than both. Deep Learning is used by some of the world's largest brands such as Google that cites it as the cornerstone of its voice image recognition algorithms. Netflix uses it to work out what you want to watch next, Amazon for what you'll buy next, and even researchers at MIT use to predict the future. There are a growing number of tech vendors entering the space who are keen to discuss how this is the next generation of data science, but what exactly is it?

Self-driving cars, have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward, and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.

In this latest Data Science Central Deep Learning Fundamentals Series webinar, we will cover the fundamentals behind TensorFlow and how to apply them within a convolutional neural network (CNN) example. The principles we will cover include CNN concepts and their impact to the accuracy and loss of your network. All these concepts will be brought to life by demonstrating how Databricks simplifies deep learning - letting you quickly access ready-to-use ML environments, as well as prepare data, and train models faster. After this session, if requested, you will receive the presentation and associated notebooks so you can run the samples yourself.

The open source silicon space has suddenly become more crowded. Shortly before Christmas, Silicon Valley AI startup Wave Computing, which is developing hardware for running deep learning applications in data centers and offices, announced plans to open source its MIPS instruction set architecture, or ISA, under what it's calling the "MIPS Open" program. When the process is completed in the first quarter, participants will have full access, with no licensing fees or royalties, to the most recent versions of the 32-bit and 64-bit MIPS ISA, along with licensing for MIPS's "hundreds of existing worldwide patents." According to Wave, open sourcing the design will open the door for semiconductor companies, developers, and universities to adopt and innovate using MIPS for next-generation system-on-chip (SoC) designs. Related: Is Open Source RISC-V Ready to Take on Intel, AMD, and ARM in the Data Center?

Entrepreneurs all know that Artificial Intelligence and Machine Learning are hot topics of focus among startups. But terms like'deep learning' and'neural networks' have been quickly absorbed into even the general public's lexicon, and today it can be difficult to tell who really knows the technology they're talking about, and who is simply pumping buzzwords. Investors do have a genuine and growing interest in funding Artificial Intelligence ventures, but slapping these terms onto a pitch deck no longer does the trick for opening the funding faucets. Recently, Founder Institute CEO Adeo Ressi sat down to discuss the future of AI and Robotics Startups and Venture Capital with a panel of renowned machine learning experts from Philips Health, Uber ATC, Carnegie Mellon University, and the Robotics Hub. The discussion touched on many of the types of questions we hear from entrepreneurs across the globe who are curious about these emerging technologies.

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.

In 2015, a man named Nigel Richards won the title of French- language Scrabble World Champion. This was especially noteworthy because Richards does not speak French. What the New Zealander had done was memorize each of the 386,000 words in the entire French Scrabble dictionary, in the space of just nine weeks. Richards' impressive feat is a useful metaphor for how artificial intelligence works--real AI, not the paranoid fantasies that some self- appointed "futurists" like to warn us about. Just as Richards committed vast troves of words to memory in order to master the domain of the Scrabble board, state-of-the-art AI--or deep learning--takes in massive amounts of data from a single domain and automatically learns from the data to make specific decisions within that domain. Deep learning can automatically optimize human-given goals--called "objective functions"--with unlimited memory and superhuman accuracy.

The quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text --- such as images, font choices, and visual layout --- we propose a joint model that combines the text content with a visual rendering of the document for document quality assessment. Experimental results over two datasets reveal that textual and visual features are complementary, achieving state-of-the-art results.

Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Given that the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model. We achieve this by globally training a Long Short-Term Memory network (LSTM) that exploits the nonlinear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting engine, we propose a systematic pre-processing framework to overcome the challenges in an E-commerce setting. We also introduce several product grouping strategies to supplement the LSTM learning schemes, in situations where sales patterns in a product portfolio are disparate. We empirically evaluate the proposed forecasting framework on a real-world online marketplace dataset from Walmart. com. Our method achieves competitive results on category level and super-departmental level datasets, outperforming state-of-the-art techniques.