"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
In this tutorial, we will package and deploy a simple model that exposes an HTTP API and serves predictions to a device managed by Synpse. Flash is a high-level deep learning framework for fast prototyping, baselining, finetuning and solving deep learning problems. It features a set of tasks for you to use for inference and finetuning out of the box, and an easy to implement API to customize every step of the process for full flexibility. Flash is built for beginners with a simple API that requires very little deep learning background, and for data scientists, Kagglers, applied ML practitioners and deep learning researchers that want a quick way to get a deep learning baseline with advanced features PyTorch Lightning offers. You can read more about the model in the image classification section.
Image Segmentation is considered a vital task in Computer Vision – along with Object Detection – as it involves understanding what is given in the image at a pixel level. It provides a comprehensive description that includes the information of the object, category, position, and shape of the given image. There are various algorithms for Image Segmentation that have been developed with applications such as scene understanding, medical image analysis, robotics, augmented reality, video surveillance, etc. The advent of Deep Learning in Computer Vision has diversified the capabilities of the existing algorithms and paved the way for new algorithms for pixel-level labeling problems such as Semantic Segmentation. These algorithms learn rich representations for the problem, including automatic pixel labeling of images in an end-to-end fashion.
GANS stand for Generative Adversarial Networks. "GANS" well it might sound complex but actually its not .Ian Good Fellow et al. published "Generative Adversarial Networks" in 2014, which was the first study to describe GANs. Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images . What exactly the term "generative" signify in the name "Generative Adversarial Network"? "Generative" describes a class of statistical models that contrasts with discriminative models.
In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. On...
Dense Layers are extremely helpful in Artificial Intelligence Algorithms. In Deep Learning models layers can be considered as the configuration of different lines that are sum up together. Basically, every layer in the Deep Learning model has its own specific significance depending upon the work assigned to it or according to its features. Hence its extremely important for AI Startups to leverage their products properly with these concepts. In today's blog, we are looking into Dense Layers and understand its working.
Researchers are developing artificial intelligence that could assess climate change tipping points. The deep learning algorithm could act as an early warning system against runaway climate change. Chris Bauch, a professor of applied mathematics at the University of Waterloo, is co-author of a recent research paper reporting results on the new deep-learning algorithm. The research looks at thresholds beyond which rapid or irreversible change happens in a system, Bauch said. "We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point," Bauch said. "Many of these tipping points are undesirable, and we'd like to prevent them if we can."
About one year ago, British newspaper The Guardian ran an article titled A robot wrote this entire article. Are you scared yet, human?, written by an Artificial Intelligence (AI)-enabled robot called GPT-3 (Generative Pre-trained Transformer 3). It is an autoregressive language model that uses deep learning to produce human-like text. GPT-3 was fed a short introduction and was instructed to write an op-ed of around 500 words in simple language, focusing on why humans have nothing to fear from AI. In response, it produced eight different essays. The Guardian picked the best parts of each and ran the edited piece.
Chris Bauch, a professor of applied mathematics at the University of Waterloo, is co-author of a recent research paper reporting results on the new deep-learning algorithm. The research looks at thresholds beyond which rapid or irreversible change happens in a system, Bauch said. "We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point," Bauch said. "Many of these tipping points are undesirable, and we'd like to prevent them if we can." Some tipping points that are often associated with run-away climate change include melting Arctic permafrost, which could release mass amounts of methane and spur further rapid heating; breakdown of oceanic current systems, which could lead to almost immediate changes in weather patterns; or ice sheet disintegration, which could lead to rapid sea-level change.
Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python. We will learn Spacy in detail and we will also explore the uses of NLP in real-life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python.
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