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Training data for effective AI deployment – Analytics India Magazine


On the other hand, deep learning techniques like GANs generate synthetic images based on original data.

Super-resolution using Deep Learning methods: A Survey


Image super-resolution refers to the process of increasing the resolution of digital images. While super-resolution can be achieved by passing multiple low-resolution images (reference images) to algorithms, this article mainly focuses on single image supervised super-resolution (SR) techniques. While this problem has been tackled using analytical methods in the Computer Vision community [1][2], recent literature shows an upsurge in the usage of deep learning techniques to perform super-resolution [3][4]. In the field of medical imaging, image resolution is often limited by the constraints on acquisition time, radiation level and hardware costs. Hence, super-resolution techniques come to the rescue, to achieve desirable perceptual quality of the images acquired in such constrained environments.

This university student's repurposed AI translates ASL into English


About two weeks ago, Priyanhali Gupta, a third year student at the Vellore Institute of Technology (VIT) in India, posted a self-created project to her LinkedIn page. In it, the computer science major showcased a demo for an AI model that can translate a few ASL signs into English. In other words, she was able to re-purpose existing code in order to meet the specifications of her ASL detector. It's worth pointing out that the model doesn't necessarily translate signs into English, but rather identifies an object displayed on webcam and then evaluates how similar said object is to the six pre-programmed phrases listed above. In a conversation with Interesting Engineering, Gupta noted that the motivating factors for her program included "her mum, who asked her'to do something now that she's studying engineering.''

Introduction to Deep Learning (The MIT Press)


This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. "I find I learn computer science material best by sitting down and writing programs," the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading.

Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning Artificial Intelligence

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework Artificial Intelligence

It is well known that translations of songs and poems not only breaks rhythm and rhyming patterns, but also results in loss of semantic information. The Bhagavad Gita is an ancient Hindu philosophical text originally written in Sanskrit that features a conversation between Lord Krishna and Arjuna prior to the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in Hinduism and known as the forefront of the Vedic corpus of Hinduism. In the last two centuries, there has been a lot of interest in Hindu philosophy by western scholars and hence the Bhagavad Gita has been translated in a number of languages. However, there is not much work that validates the quality of the English translations. Recent progress of language models powered by deep learning has enabled not only translations but better understanding of language and texts with semantic and sentiment analysis. Our work is motivated by the recent progress of language models powered by deep learning methods. In this paper, we compare selected translations (mostly from Sanskrit to English) of the Bhagavad Gita using semantic and sentiment analyses. We use hand-labelled sentiment dataset for tuning state-of-art deep learning-based language model known as \textit{bidirectional encoder representations from transformers} (BERT). We use novel sentence embedding models to provide semantic analysis for selected chapters and verses across translations. Finally, we use the aforementioned models for sentiment and semantic analyses and provide visualisation of results. Our results show that although the style and vocabulary in the respective Bhagavad Gita translations vary widely, the sentiment analysis and semantic similarity shows that the message conveyed are mostly similar across the translations.

Machine Learning: Algorithms, Models, and Applications Artificial Intelligence

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

Semantic Segmentation of Legal Documents via Rhetorical Roles Artificial Intelligence

Legal documents are unstructured, use legal jargon, and have considerable length, making it difficult to process automatically via conventional text processing techniques. A legal document processing system would benefit substantially if the documents could be semantically segmented into coherent units of information. This paper proposes a Rhetorical Roles (RR) system for segmenting a legal document into semantically coherent units: facts, arguments, statute, issue, precedent, ruling, and ratio. With the help of legal experts, we propose a set of 13 fine-grained rhetorical role labels and create a new corpus of legal documents annotated with the proposed RR. We develop a system for segmenting a document into rhetorical role units. In particular, we develop a multitask learning-based deep learning model with document rhetorical role label shift as an auxiliary task for segmenting a legal document. We experiment extensively with various deep learning models for predicting rhetorical roles in a document, and the proposed model shows superior performance over the existing models. Further, we apply RR for predicting the judgment of legal cases and show that the use of RR enhances the prediction compared to the transformer-based models.

Job Oriented Best Deep Learning Training Course In Delhi


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Ten Visions for Our Future


This is a summary of the book "AI 2041" -- By Kai-Fu Lee and Chen Qiufan. This book gives a provocative work of speculative fiction with analysis that explores the ways in which AI will shake up our world over the next twenty years. It often feels as if the modern world is already a science fiction fantasy. Who'd have guessed that one day you'd be able to request a song from your household appliances, or that you'd have a computer in your pocket that would remind you when it's time to go for a walk? But this is only the start. The advancement of deep learning and natural language acquisition will accelerate AI advancements. Self-driving cars and weapons are already in the works. Deepfake films and virtual reality games are getting so convincing that it's difficult to tell the difference between fiction and reality. Each of the following concept begins with a short, fictitious scenario about what the world may look like in 2041 -- that is, after another 20 years of AI progress – followed by a study of the societal implications of these advances. They'll work together to help you get ready for the AI revolution. In 2041, Nayana's family in Mumbai signed up with a new insurance business called Ganesh Insurance, which drastically reduced their insurance payments.