Agriculture is a very visual industry. At every stage of the food value chain, industry workers with varying education levels, experience, and age are performing thousands of tasks and making decisions- primarily based on visual inspection. The quality and accuracy of these actions and decisions can vary greatly and have real economic consequences. Industry innovators have responded with a "more is better" approach: add more sensors, add more automation, add more inputs like drones or satellite imagery. The result is the availability of more agricultural data than ever before, for growers who are less equipped to make sense of it all.

In this article, we will be looking at what is medical imaging, the different applications and use-cases of medical imaging, how artificial intelligence and deep learning is aiding the healthcare industry towards early and more accurate diagnosis. We will review literature about how machine learning is being applied in different spheres of medical imaging and in the end implement a binary classifier to diagnose diabetic retinopathy. Medical imaging consists of set of processes or techniques to create visual representations of the interior parts of the body such as organs or tissues for clinical purposes to monitor health, diagnose and treat diseases and injuries. Moreover, it also helps in creating database of anatomy and physiology. Owing to the advancements in the field today medical imaging has the ability to achieve information of human body for many useful clinical applications. Different types of medical imaging technology gives different information about the area of the body to be studied or medically treated. Organisations incorporating the medical imaging devices include freestanding radiology and pathology facilities as well as clinics and hospitals. Major manufacturers of these medical imaging devices include Fujifilm, GE, Siemens Healthineers, Philips, Toshiba, Hitachi and Samsung.

Facebook's Pytorch seems to have become a favoured choice among deep learning researchers and developers; however, TensorFlow is still believed to hold the top position for building machine learning models, and the debate continues. Pytorch is the second-fastest-growing open source project on Github and is famous for its advanced indexing, functions, integration support, API simplicity, and style. And, therefore, many AI and ML tech giants are either planning to switch to PyTorch or have already adopted it. Many companies have also been using Pytorch's advantages for research and production. OpenAI is the recent addition to the community of the tech giants that are using PyTorch; ending its TensorFlow usage. To contribute more towards the Pytorch community, OpenAI intends on introducing Deep RL educational resources on Pytorch.

You read about it constantly. It seems like everyone's doing it, but does anyone actually know what it is? Today, you can't go more than a day without hearing about artificial intelligence (A.I.) or machine learning (ML), but these concepts have actually been around for a long time. New advancements in computing power and algorithms have brought about a recent resurgence in the field, though. Now, it seems as if everyone thinks A.I. will change the world.

BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high-performance edge AI technology, today announced that it will present its revolutionary new breed of neuromorphic processing IP and Device in two sessions at the tinyML Summit at the Samsung Strategy & Innovation Center in San Jose, California February 12-13. In the Poster Session, "Bio-Inspired Edge Learning on the Akida Event-Based Neural Processor," representatives from BrainChip will explain to attendees how the company's Akida Neuromorphic System-on-Chip processes standard vision CNNs using industry standard flows and distinguishes itself from traditional deep-learning accelerators through key features of design choices and bio-inspired learning algorithm. These features allow Akida to require 40 to 60 percent fewer computations to process a given CNN when compared to a DLA, as well as allowing it to perform learning directly on the chip. BrainChip will also demonstrate "On-Chip Learning with Akida" in a presentation by Senior Field Applications Engineer Chris Anastasi. The demonstration will involve capturing a few hand gestures and hand positions from the audience using a Dynamic Vision Sensor camera and performing live learning and classification using the Akida neuromorphic platform.

Arabic is the 4th most-used language on the Internet, and its growing presence on social media is providing ample resources for the study of Arabic-language online communities at scale. There are however few tools currently available that can derive valuable insights from this data for decision making, guiding policies, aiding in responses, etc. Is that about to change? The performance of natural language processing (NLP) systems has dramatically improved on tasks such as reading comprehension and natural language inference, and with these advances have come many new application scenarios for the tech. Unsurprisingly, English is where most NLP R&D has been focused.

The first time I attempted to study recurrent neural networks, I made the mistake of trying to learn the theory behind things like LSTMs and GRUs first. In summary, you don't need to understand everything about the specific architecture of an LSTM cell; as a human, it shouldn't be your job to understand it. Just keep in mind what the LSTM cell is meant to do: allow past information to be reinjected at a later time. This was the author of the library Keras (Francois Chollet), an expert in deep learning, telling me I didn't need to understand everything at the foundational level! I realized that my mistake had been starting at the bottom, with the theory, instead of just trying to build a recurrent neural network.

Rice University engineers have created a deep learning computer system that taught itself to accurately predict extreme weather events, like heat waves, up to five days in advance using minimal information about current weather conditions. Ironically, Rice's self-learning "capsule neural network" uses an analog method of weather forecasting that computers made obsolete in the 1950s. During training, it examines hundreds of pairs of maps. Each map shows surface temperatures and air pressures at five-kilometers height, and each pair shows those conditions several days apart. The training includes scenarios that produced extreme weather -- extended hot and cold spells that can lead to deadly heat waves and winter storms.

PyTorch is an open source machine learning library. The name PyTorch is derived from its main programming language, Python, and Torch, the library on which it is based. Since PyTorch's release in 2016, it has grown in popularity with developers due to its ease of use, flexibility, easy debugging, fast speed, and community support. Developed by Facebook, PyTorch is similar to Google's TensorFlow in that it runs on tensors, but instead of using static computation graphs like TensorFlow, it utilizes dynamic computation graphs. If you are interested in learning PyTorch, the following list of resources can help you get started.

To date, most state-of-the-art sequence modelling architectures use attention to build generative models for language based tasks. Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$. Alternatively, they utilize depthwise convolutions with softmax normalized kernels of size $k$ acting as a limited-window self-attention, resulting in time complexity of $O(k{\cdot}n)$. In this paper, we introduce Time-aware Large Kernel (TaLK) Convolutions, a novel adaptive convolution operation that learns to predict the size of a summation kernel instead of using the fixed-sized kernel matrix. This method yields a time complexity of $O(n)$, effectively making the sequence encoding process linear to the number of tokens. We evaluate the proposed method on large-scale standard machine translation and language modelling datasets and show that TaLK Convolutions constitute an efficient improvement over other attention/convolution based approaches.