deep-learning

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In this blog, we will build a Flask web app that can input any long piece of information such as a blog or news article and summarize it into just five lines! Text summarization is an NLP(Natural Language Processing) task. SBERT(Sentence-BERT) has been used to achieve the same. By the end of the article, you will learn how to integrate AI models and specifically pre-trained BERT models with Flask web technology as well!

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TorchDrug is a machine learning platform designed for drug discovery, covering techniques from graph machine learning (graph neural networks, geometric deep learning & knowledge graphs), deep generative models to reinforcement learning. It provides a comprehensive and flexible interface to support rapid prototyping of drug discovery models in PyTorch. Link

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Курс по графам знаний (Knowledge Graphs) и как их готовить в 2021 году. На русском языке. Link

It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.

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Advancements in deep learning have been rapid over the past decade. While the discovery of neural networks happened almost six decades ago with the invention of the first artificial neural network in 1958 by psychologist Frank Rosenblatt (called the “perceptron”), the developments in the field did not gain true popularity until about a decade ago. The most popular achievement in 2009 was the creation of ImageNet. ImageNet is a humungous visual dataset that has led to some of the best modern-day deep learning and computer vision projects.

Anomagram is an interactive visualization tool for exploring how a deep learning model can be applied to the task of anomaly detection (on stationary data). Given an ECG signal sample, an autoencoder model (running live in your browser) can predict if it is normal or abnormal. To try it out, click any of the test ECG signals from the ECG5000 dataset below, or better still, draw a signal to see the model’s prediction!

This post is a simple tutorial for how to use a variant of BERT to classify sentences. This is an example that is basic enough as a first intro, yet advanced enough to showcase some of the key concepts involved. Link