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Posts Tagged ‘machine learning’

Attention Mechanism: Benefits and Applications

Recurrent Neural Networks (RNNs) are powerful neural network architectures used for modeling sequences. LSTM (Long Short Term Memory) based RNNs are surprisingly good at capturing long-term dependencies in the sequences. A barebones sequence-to-sequence/encoder-decoder architecture performs incredibly well in tasks like Machine Translation. A typical sequence-sequence architecture consists of an encoder and a decoder RNN. The…

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Privacy and Machine Learning: Concerns and Possible Solutions

AI and its applications are here to stay, even if they bring with them new challenges, as their advantages seem to outweigh the risks. However, in the light of latest controversies and concerns over privacy breach by social companies, the efforts to ensure privacy of users need to be redoubled. Arjun Bahuguna discusses the solutions that manage the vulnerabilities of machine learning and instead leverages its strength to guard information privacy.

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Disruptive Innovation of Blockchain and Evolution of Pharmaceutical Ecosystem in Data Integrity and Transparency

An interconnected ecosystem is the need of the hour in the pharmaceutical world, where it is more important than ever to enable sharing of information among patients, researchers, and manufacturers and supply chain organizations. However, sharing entails risk to data security. Tushar Sinkar discusses the merits of using blockchain to solve the data security issue and facilitate easy sharing of information.

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Quantization and the Need for TPUs

Archana dives into the world of TPUs and discusses quantization, the process that is really responsible for making predictions in a neural network, lending a magical touch to the machine learning process. Read on to understand how quantization can be both a boon and a bane, depending on where it is used.

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An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text

Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model. Our objective in…

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Applications of Signal Processing in Machine Learning

machine learning

Data is available abundantly in today’s world. However, it is noisy most of the time. In this article, Archana Iyer discusses some filter processing techniques that can help us get a better quality of data. With the advent of IoT, many types of medical data are now available in the form of sensor data. This…

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Capsule Networks and the Limitations of CNNs

machine learning

Convolutional Neural Networks are considered the State-of-the-Art in computer vision related Machine Learning tasks. Soham Chatterjee highlights the limitations of CNNs and discusses alternate models that closely mirror the way the human brain work. He uses Professor Geoffrey Hinton’s paper, Dynamic Routing Between Capsules, to establish certain points. Convolutional Neural Networks, popularly called CNNs, have…

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Interpretable Models are the Key to Increased Adoptability of Machine Learning

machine learning

Machine learning has helped drive many technological advancements. There are two models of machine learning, which have their own pros and cons. Malaikannan Sankarasubbu discusses the way interpretable models can increase the machine learning among a wider base of communities. Machine Learning and Deep Learning are responsible for lot of technology advancements in the last…

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How to Turbo-charge your Machine Learning Model

machine learning

Processing and analysing data calls for great computing power and advanced technology stacks. The evolution we witnessing today has been gradual. In this blog, Narendra Shukla reminisces this journey with us. Ingesting massive amount of data requires exceptionally fast computing power. The appetite for radically advancing the computing speed increased with the need for processing…

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