As we have seen, GPT-3 can write from scratch -- and in our series "The Art To Start", you will learn how to "scratch". Yet, it also works without any prompt. You can click "submit" and be surprised about the results. Without any prompt, GPT-3 chooses entirely random contents. Back in the 1920ies, Dadaists and Surrealists (most prominently: André Breton) examined their creativity using the method of Écriture Automatique: "automatic writing", without thinking about their results (censoring).

It's been a busy few days at NeurIPS 2020 so far with all manner of talks, workshops, tutorials and socials on offer. This selection of tweets gives a flavour of the various events and discussions taking place. Go watch it right now, you won't regret it! Interesting talk by Chris Bishop at #NeurIPS2020 Basic or Applied research is not a 1D space. Next up at #NeurIPS2020: Shafi Goldwasser presenting on three works about privacy, verifiability, and robustness in machine learning.

This tutorial covers the entire ML process, from data ingestion, pre-processing, model training, hyper-parameter fitting, predicting and storing the model for later use. Let's see the whole picture Recreating the entire experiment without PyCaret requires more than 100 lines of code in most libraries. The library also allows you to do more advanced things, such as advanced pre-processing, ensembling, generalized stacking, and other techniques that allow you to fully customize the ML pipeline and are a must for any data scientist. PyCaret is an open source, low-level library for ML with Python that allows you to go from preparing your data to deploying your model in minutes. Allows scientists and data analysts to perform iterative data science experiments from start to finish efficiently and allows them to reach conclusions faster because much less time is spent on programming. When working on a data science project, it usually takes a long time to understand the data (EDA and feature engineering). So, what if we could cut the time we spend on the modeling part of the project in half?

Reference textbooks for different parts of the course are "Pattern Recognition and Machine Learning" by Chris Bishop (Springer 2006) and "Probabilistic Graphical Models" by Daphne Koller and Nir Friedman (MIT Press 2009) and "Deep Learning" by Goodfellow, Bengio and Courville (MIT Press 2016).

When it comes to innovations in AI, Tier-1 institutes such as IIT have been trying to leave no stone unturned. IITs have been performing a lot of research work in the field of emerging technologies like AI, machine learning, blockchain, among others. The institutes are also joining hands with the government and various other prominent organisations to launch the Centre of Excellence (CoE), Research & Development Centres (R&Ds), among others. In this list, we have curated the top AI initiatives, in no particular order, by IIT in the year 2020. In January, Indian Institute of Technology, Kharagpur has evolved an AI-aided method to read legal judgements.

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Just as the name implies, data science is a branch of science that applies the scientific method to data with the goal of studying the relationships between different features and drawing out meaningful conclusions based on these relationships. Data is, therefore, the key component in data science. A dataset is a particular instance of data that is used for analysis or model building at any given time. A dataset comes in different flavors such as numerical data, categorical data, text data, image data, voice data, and video data. A dataset could be static (not changing) or dynamic (changes with time, for example, stock prices).

Due to the success of deep learning and its growing job market, students and researchers from many areas are getting interested in learning about deep learning technologies. Visualization has proven to be of great help during this learning process, while most current educational visualizations are targeted towards one specific architecture or use case. Unfortunately, recurrent neural networks (RNNs), which are capable of processing sequential data, are not covered yet, despite the fact that tasks on sequential data, such as text and function analysis, are at the forefront of deep learning research. Therefore, we propose exploRNN, the first interactively explorable, educational visualization for RNNs. exploRNN allows for interactive experimentation with RNNs, and provides in-depth information on their functionality and behavior during training. By defining educational objectives targeted towards understanding RNNs, and using these as guidelines throughout the visual design process, we have designed exploRNN to communicate the most important concepts of RNNs directly within a web browser. By means of exploRNN, we provide an overview of the training process of RNNs at a coarse level, while also allowing detailed inspection of the data-flow within LSTM cells. Within this paper, we motivate our design of exploRNN, detail its realization, and discuss the results of a user study investigating the benefits of exploRNN.

In this Guided Project, you will: How to get familiar with Machine Learning basics and how to start a model prediction using basic supervised Machine Learning models. Build, train, test and evaluate the performance of some models. In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA).

A recently developed data science pathway for fourth-year radiology residents will help prepare the next generation of radiologists to lead the way into the era of artificial intelligence and machine learning (AI-ML), according to a special report published in Radiology: Artificial Intelligence. AI-ML has the potential to transform medicine by delivering better and more efficient healthcare. Applications in radiology are already arriving at a staggering rate. Yet organized AI-ML curricula are limited to a few institutions and formal training opportunities are lacking. Three senior radiology residents at Brigham and Women's Hospital (BWH) in Boston recently helped devise a data science pathway to provide a well-rounded introductory experience in AI-ML for fourth-year residents.