Hands-on to ReAgent: End-to-End Platform for Applied Reinforcement Learning

Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of this framework is to make the development & experimentation of deep reinforcement algorithms fast. ReAgent is built on Python. It uses PyTorch framework for data modelling and training and TorchScript for serving.

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Guide To TAPAS (TAble PArSing) – A technique to retrieve information from Tabular Data using NLP

One of the most common forms of data that exists today is tabular data (structured data).In order to extract information from tabular data, you use Python libraries like Pandas or SQL-like languages. Google has recently open-sourced one of their models called ‘TAPAS’ (for TAble PArSing) wherein you can ask questions about your data in natural language.

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Complete Guide to AutoGL -The latest AutoML Framework for graph datasets

Photographer keys hanging

Creating algorithms is difficult and time-consuming. This specific problem has inspired researchers to develop some productivity tools to help young members in this domain. This has given birth to a revolutionary field in Data Science called Auto Machine Learning(AutoML). AutoML provides methods and processes to make Machine Learning available to non-Machine Learning experts, to improve efficiency of Machine Learning and speed up the research.

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What is a Time Series GAN?

This post was originally published by Sejuti Das at Analytics India Magazine Identifying anomalies in time series data can be daunting, thanks to the vague definition of anomalies, lack of labelled data, and highly complex…

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Microsoft releases Unadversarial examples: Designing objects for robust vision – a complete hands-on guide

In a recent work by Microsoft Research, a new framework is introduced which can address these problems of data models to create “unadversarial objects,” inputs that are optimized particularly for more robust model performance. This newly proposed approach for image recognition/classification methods helps in predicting better in the case of unforeseen corruptions or distribution shifts.

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Hands-on Guide to Adversarial Robustness Toolbox (ART): Protect your Neural Networks against hacking

Adversarial Robustness Toolbox

Machine Learning models can be exposed to the threat to jeopardise with the predictions. Such attacks on deployment ends have been seen time and again and thus needed to be addressed accurately. AI security is most necessary for enterprise AI systems where data storage is mostly in tabular forms, and data privacy policies are at stake. The Adversarial Robustness Toolbox(ART) is a Python library which is one of the complete resources providing developers and researchers for evaluating the robustness of deep neural networks against adversarial attacks.

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A complete Learning Path to Data Labelling & Annotation (with Guide to 15 major Tools)

With the advancements in deep learning algorithms, computer vision and NLP have greatly evolved and done wonders around the world of AI. Along with this AutoML has also grown. This has led many industries to adopt AI smoothly and make efficient use of it in various use cases.

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Informer: LSTF (Long Sequence Time-Series Forecasting) Model

Time series forecasting is the most complex technique to solve and forecast with the help of traditional methods of using statistics for time series forecasting the data. But now as the neural network has been introduced and many CNN-based time series forecasting models have been developed, you can see how accurate and easy it became to predict future values based on historical time-series data points. Long short term memory(LSTM) is the one which is used for long-term forecasting. But there are many problems with LSTM which leads to further research in LSTF…

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