"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models. This book is to accompany the usual free tutorial videos and sample code from youtube.com/sentdex. This topic is one that warrants multiple mediums and sittings. Having something like a hard copy that you can make notes in, or access without your computer/offline is extremely helpful.

Deep learning has been successfully showing promising results in plant disease detection, fruit counting, yield estimation, and gaining an increasing interest in agriculture. Deep learning models are generally based on several millions of parameters that generate exceptionally large weight matrices. The latter requires large memory and computational power for training, testing, and deploying. Unfortunately, these requirements make it difficult to deploy on low-cost devices with limited resources that are present at the fieldwork. In addition, the lack or the bad quality of connectivity in farms does not allow remote computation. An approach that has been used to save memory and speed up the processing is to compress the models. In this work, we tackle the challenges related to the resource limitation by compressing some state-of-the-art models very often used in image classification. For this we apply model pruning and quantization to LeNet5, VGG16, and AlexNet. Original and compressed models were applied to the benchmark of plant seedling classification (V2 Plant Seedlings Dataset) and Flavia database. Results reveal that it is possible to compress the size of these models by a factor of 38 and to reduce the FLOPs of VGG16 by a factor of 99 without considerable loss of accuracy.

To date, it might well be the most effective and useful algorithm -- or family of algorithms -- that humanity ever invented. Ever-improving methods for erecting models of how the world works and then testing those models against evidence make it possible to distinguish good ideas from bad. Step-by-step, humanity's understanding of the universe, the world, and itself, has grown. The Artificial Intelligence revolution, however, could well overturn how good ideas are sifted from bad and subvert science's ultimate goal of understanding. The claim that AI could undermine scientific understanding, or even make it obsolete, is far from new.

Artificial intelligence chip company Hailo Technologies Ltd. said today it's launching two new acceleration modules that will boost the processing capabilities of edge devices that run its specialist hardware. Hailo burst onto the AI scene in 2019 with a customized processor for running deep learning workloads at the edge of the network. The company, which is primarily focused on the automotive sector, said at the time that its Hailo-8 Deep Learning chip enables devices such as autonomous vehicles, smart cameras, drones and AR/VR platforms to run sophisticated deep learning applications at the edge that could previously be hosted only in cloud data centers. The Hailo-8 processor, which is smaller than a penny, was built from the ground up with completely redesigned memory, control and compute architecture components that enable "higher performance, lower power and minimal latency." Hailo also provides a software development kit for developers to build apps customized for the hardware.

Transformers have become the defacto standard for any NLP tasks nowadays. Not only that, but they are now also being used in Computer Vision and to generate music. I am sure you would all have heard about the GPT3 Transformer and its applications thereof. But all these things aside, they are still hard to understand as ever. It has taken me multiple readings through the Google research paper that first introduced transformers along with just so many blog posts to really understand how a transformer works. So, I thought of putting the whole idea down in as simple words as possible along with some very basic Math and some puns as I am a proponent of having some fun while learning. I will try to keep both the jargon and the technicality to a minimum, yet it is such a topic that I could only do so much. And my goal is to make the reader understand even the goriest details of Transformer by the end of this post. Also, this is officially my longest post both in terms of time taken to write it as well as the length of the post. Hence, I will advise you to Grab A Coffee. So, here goes -- This post will be a highly conversational one and it is about "Decoding The Transformer".

The Covid-19 tragedy and crisis has placed the spotlight on the healthcare systems around the world and placed an additional strain on systems that in many cases were already under stress to meet demand and led to a growth in digital medicine. A video from the BBC observers that Covid-19 brings remote medicine revolution to the UK "Apps which allow doctors to connect with patients remotely have been available for a while, but the coronavirus pandemic has seen doctors finding new ways to consult with critical patient care, including reviewing scans and X-rays from home." McKinsey in an article relating to the US healthcare situation and entitled "Preparing for the next normal now: How health systems can adopt a growth transformation in the COVID-19 world" state that "Covid-19 unprecedented impact on health, economies, and daily life has created a humanitarian crisis. Health systems have been at the epicenter of the fight against COVID-19, and have had to balance the need to alleviate suffering and save lives with substantial financial pressures." "Health systems' income statements are likely to see negative pressure as a result of the COVID-19 crisis. While health systems have ramped up capacity to handle COVID-19 cases and incurred additional costs to procure personal protective equipment and operationalize surge capacity plans, they also have had declines of up to 70 percent in surgical volume and 60 percent in emergency department traffic. In a recent McKinsey survey of health system CFOs, more than 90 percent of respondents reported that COVID-19 will have a negative financial impact, even after accounting for federal and state funding."

Researchers have demonstrated that an artificial intelligence (AI) algorithm could be trained to classify Covid-19 pneumonia in computed tomography (CT) scans with up to 90 per cent accuracy. Also, it correctly identifies positive cases 84 per cent of the time and negative cases 93 per cent of the time. The study, recently published in Nature Communications, shows the new technique can also overcome some of the challenges of current testing. "We demonstrated that a deep learning-based AI approach can serve as a standardized and objective tool to assist healthcare systems as well as patients," said study author Ulas Bagci from the University of Central Florida in the US. "It can be used as a complementary test tool in very specific limited populations, and it can be used rapidly and at large scale in the unfortunate event of a recurrent outbreak," Bagci added.

After being named sexiest job of the 21st Century" by Harvard Business Review, data science has blended the enthusiasm of the overall population. Numerous individuals are fascinated by the job and can't help thinking about how they themselves can become data scientists. There are endless tools, courses, and applications for people to learn data science, however, let's be honest: for somebody new to the field, every one of these options can appear to be a jungle of complex information. Microsoft has been a major player in the data science industry after Azure and it's machine learning tools have been gradually ruling as the biggest service provider in the cloud-computing market. Therefore, Microsoft has been working out its data science team gradually in recent years to get one of the greatest companies hiring for data scientists.

Mass Eye and Ear researchers have discovered a unique diagnostic tool that can detect dystonia from MRI scans. It is the first technology of its kind to provide an objective diagnosis of the disorder. Dystonia is a potentially disabling neurological condition which causes involuntary muscle contractions, driving to abnormal movements and postures. It is often mistreated and sometimes takes people up to 10 years to get a correct diagnosis. A new study by PNAS researches shows that they have developed an AI-based deep learning platform on September 28, called DystoniaNet to compare brain MRIs of 612 people.

Since it was unveiled earlier this year, the new AI-based language generating software GPT-3 has attracted much attention for its ability to produce passages of writing that are convincingly human-like. Some have even suggested that the program, created by Elon Musk's OpenAI, may be considered or appears to exhibit, something like artificial general intelligence (AGI), the ability to understand or perform any task a human can. This breathless coverage reveals a natural yet aberrant collusion in people's minds between the appearance of language and the capacity to think. Language and thought, though obviously not the same, are strongly and intimately related. And some people tend to assume that language is the ultimate sign of thought.