These draft notebooks cover an introduction to deep learning, fastai, and PyTorch. These notebooks will be used for a course we're teaching in San Francisco from March 2020, and will be available as a MOOC from around July 2020. In addition, our plan is that these notebooks will form the basis of this book, which you can pre-order. It will not have the same GPL restrictions that are on this draft. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use.

The new system, dubbed DeepSPM, bridges the gap between nanoscience, automation and artificial intelligence (AI), and firmly establishes the use of machine learning for experimental scientific research. "Optimising SPM data acquisition can be very tedious. This optimisation process is usually performed by the human experimentalist, and is rarely reported," says FLEET Chief Investigator Dr Agustin Schiffrin (Monash University). "Our new AI-driven system can operate and acquire optimal SPM data autonomously, for multiple straight days, and without any human supervision." The advance brings advanced SPM methodologies such as atomically-precise nanofabrication and high-throughput data acquisition closer to a fully automated turnkey application.

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.

Like any tool, technology can be used for both good and bad. And sometimes, that bad is inadvertent; tech in the form of airplanes helped expedite the spread of the coronavirus around the world. But fortunately, technology will also aid in stopping this pandemic crisis. A few weeks ago, we wrote about how the San Francisco-based company BlueDot utilized artificial intelligence (AI) to warn the general public about the dangers of COVID-19 well ahead of health officials. In case you missed it, you can read it here. Examples like BlueDot give us hope that emerging tools like AI can help humanity tackle problems like the coronavirus in unprecedented ways.

Rensselaer Polytechnic Institute is turning one of the most powerful supercomputers in the world against COVID-19. AiMOS, short for Artificial Intelligence Multiprocessing Optimized System, can perform eight quadrillion calculations per second and is uniquely capable of exploring new applications in artificial intelligence. RPI is offering the supercomputer to the research community--including government entities, academic universities, national labs, and private businesses--to support new coronavirus research. To combat the devastating effects of this pandemic, we must be able to fully grasp the complexities and interconnectedness of biological systems and epidemiological data, as researchers work to develop therapeutic interventions and address gaps in our knowledge. AiMOS is the most powerful supercomputer at a private university, the third-most energy-efficient supercomputer in the world, and the 24th most-powerful in the world overall.

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns.

As artificial intelligence has become a growing force in business, today's top AI companies are leaders in this emerging technology. Often leveraging cloud computing, AI companies mix and match myriad technologies. Foremost among these is machine learning, but today's AI leading firms tech ranging from predictive analytics to business intelligence to data warehouse tools to deep learning. Entire industries are being reshaped by AI. RPA companies have completely shifted their platforms. AI in healthcare is changing patient care in numerous – and major – ways. AI companies are attracting massive investment from venture capitalist firms and giant firms like Microsoft and Google. Academic AI research is growing, as are AI job openings across a multitude of industries. All of this is documented in the AI Index, produced by Stanford University's Human-Centered AI Institute. Consulting giant Accenture believes AI has the potential to boost rates of profitability by an average of 38 percentage points and could lead to an economic boost of $14 trillion in additional gross value added (GVA) by 2035. In truth, artificial intelligence holds not just possibilities, but a plethora of risks. "It will have a huge economic impact but also change society, and it's hard to make strong predictions, but clearly job markets will be affected," said Yoshua Bengio, a professor at the University of Montreal, and head of the Montreal Institute for Learning Algorithms. To keep up with the AI market, we have updated our list of top AI companies playing a key role in shaping the future of AI. We feature artificial intelligence companies that are commercially successful as well as those that have invested significantly in artificial intelligence. AI companies in the years ahead are forecast to see exponential growth in deep learning, machine learning and natural language processing.

Time series models predominantly, over the years, have focussed on individual time series via local models. This changed with the popularisation of deep learning techniques. This was also supported by the increase of temporal data availability, which led to many deep learning-based time series algorithms. Due to their natural temporal ordering, time-series data are present in almost every task that is registered, taking into account some notion of ordering. From electronic health records and human activity recognition to acoustic scene classification and cyber-security, time series is encountered in many real-world applications.

This article aims to simplify and amplify your understanding of neural networks. The article holds good for the audience that ranges between'I have no clue about neural networks' to'I have implemented the backpropagation algorithm'. In this article, we will go in-depth and ensure understandability. Neural networks have been the go-to solution to solve many industrial problems. There are several explanations provided for the effectiveness of the neural networks.