In this tutorial, you will learn how to break deep learning models using image-based adversarial attacks. We will implement our adversarial attacks using the Keras and TensorFlow deep learning libraries. Imagine it's twenty years from now. Nearly all cars and trucks on the road have been replaced with autonomous vehicles, powered by Artificial Intelligence, deep learning, and computer vision -- every turn, lane switch, acceleration, and brake is powered by a deep neural network. Now, imagine you're on the highway. You're sitting in the "driver's seat" (is it really a "driver's seat" if the car is doing the driving?) while your spouse is in the passenger seat, and your kids are in the back. Looking ahead, you see a large sticker plastered on the lane your car is driving in.

'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist.

There is a vast and growing number of Data Science resources. It can be hard to find the best ones for you. It may even be hard to find the right "Roadmap for Data Science" or "Top Skills to Learn for Data Science". I don't claim to have the best resources or the correct path to a career in Data Science. What I have is a list of useful resources and if even one of them furthers your learning my goal is accomplished.

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).