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Aishwarya Verma

New Delhi, India
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About Aishwarya
Aishwarya Verma is a researcher & writer based in New Delhi, India.
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How Lyft’s Library for Self-driving Simulation Works

15 Mar 2021  |  analyticsindiamag.com
Lyft's self-driving division has developed a framework for machine learning-based solutions in autonomous vehicle prediction, planning, and simulation. The framework includes datasets for training models, the L5Kit library for converting driving scenes into machine learning problems, and examples for visualization and training. The software is open to external contributors and aims to enhance self-driving technology through data-driven approaches.

Guide to Scalable and Robust Bayesian Optimization with Dragonfly

13 Mar 2021  |  analyticsindiamag.com
Dragonfly is an open-source Python framework for scalable and robust Bayesian optimization, developed by researchers from Carnegie Mellon University. The framework is designed to optimize expensive functions with high efficiency and has outperformed many existing systems. It supports Python 2 and 3, is compatible with Mac, Linux, and Windows, and can be installed via PyPI. Dragonfly offers scalability for handling higher dimensional domains and robustness by considering a set of values for acquisition choices and model parameters. It can be used through the command line, Python code, or an ask-tell interface, and includes features for multi-fidelity evaluations, parallel evaluations, and evolutionary algorithms.

Reinventing Deep Learning Operation Via Einops

09 Mar 2021  |  analyticsindiamag.com
Einops, short for Einstein-Inspired Notation for operations, is an open-source Python framework designed to improve the way deep learning code is written. It introduces new notation and operations, aiming to enhance code readability and reliability. Einops is compatible with several frameworks, including numpy, pytorch, tensorflow, and others. The article provides examples of basic operations such as rearrange, reduce, and repeat, and demonstrates their use in deep learning tasks. It also mentions the availability of tutorials and further references for those interested in using Einops.

Guide to MBIRL - Model Based Inverse Reinforcement Learning

26 Feb 2021  |  analyticsindiamag.com
The article discusses the Model-Based Inverse Reinforcement Learning (MBIRL) algorithm, which learns loss functions and rewards through gradient-based bi-level optimization. It addresses challenges faced by previous IRL approaches by using visual data for training cost functions, learning a differentiable model of dynamics, and stabilizing the cost function learning process. The MBIRL framework is a collaborative work by researchers from Facebook AI Research, Max Planck Institute for Intelligent Systems, University of Edinburgh, and University of Pennsylvania, and was presented at CoRL 2020. The article also provides instructions for installing and running the MBIRL algorithm.

A New Hyperparameter Optimization Tool

01 Feb 2021  |  Analytics India Magazine
Optuna is an advanced hyperparameter optimization framework developed by the Japanese AI company Preferred Networks. It is designed to handle machine learning and non-machine learning tasks with an imperative interface supporting Python. Optuna offers intuitive construction of search spaces, efficient sampling and pruning strategies, and is lightweight, versatile, and scalable. It also features a dashboard for analysis. The article provides an overview of Optuna's features, installation instructions, and demos for optimizing machine learning models, including the use of pruning algorithms and its application to non-ML tasks.

An Automatic ML Model Creation Framework

31 Jan 2021  |  analyticsindiamag.com
LightAutoML (LAMA) is an open-source Python framework developed by Sberbank AI Lab's AutoML group, aimed at providing end-to-end solutions for machine learning tasks. It supports binary classification, multiclass classification, and regression on tabular and text data. LAMA offers predefined pipelines and allows for custom pipeline creation, including hyperparameter tuning, data processing, and advanced feature selection. The framework is designed to be lightweight and efficient, addressing challenges such as preprocessing diverse data types, handling large datasets, and ensuring model interpretability. Key advantages include faster implementation, improved model quality, and automatic validation and benchmarking. The article provides a detailed guide on installing LAMA, creating custom pipelines, and using its features for various ML tasks.

Guide to AI Fairness 360: An Open Source Toolkit for Detection And Mitigation of Bias in ML Models

27 Jan 2021  |  analyticsindiamag.com
AI Fairness 360 is an open-source toolkit developed by IBM researchers to detect and mitigate bias in machine learning models. It provides a comprehensive set of bias metrics, mitigation algorithms, and explanations to help create fairer AI systems. The toolkit, available in Python and R, includes an architecture for dataset representation, algorithms for bias detection, mitigation, explanation, and an interactive web interface. The article also covers a tutorial on mitigating age bias in credit decisions using the German Credit Dataset.

Hands-On Guide to TadGAN (With Python Codes)

22 Jan 2021  |  analyticsindiamag.com
Anomaly detection in data science has seen significant advancements, particularly with the rise of temporal data. Traditional methods, while effective, often struggle with non-linearity and outliers. Generative Adversarial Networks (GANs) offer a solution by capturing data's hidden distribution, though they are not strong learners. Researchers from MIT have developed TadGAN, a model combining deep learning and GAN approaches for time series anomaly detection. This guide provides a practical implementation of TadGAN using the Orion library, detailing steps from installation to data preparation, model training, and evaluation. The NYC taxi dataset is used to demonstrate the process, highlighting the model's ability to detect anomalies. The guide also covers error computation, thresholding methods, and evaluation techniques, emphasizing the effectiveness of TadGAN in anomaly detection.

Complete Guide To AutoGL -The Latest AutoML Framework For Graph Datasets

20 Jan 2021  |  analyticsindiamag.com
AutoGL, developed by Tsinghua University, is an AutoML framework designed for graph datasets, handling various stages from feature engineering to model ensembling. The article provides a tutorial on installation, dataset import, feature engineering, model selection, training, hyperparameter optimization, and ensemble methods. It also previews upcoming features such as Neural Architecture Search and support for more graph tasks and libraries.

Hands-on Guide to Reformer – The Efficient Transformer

20 Jan 2021  |  analyticsindiamag.com
The Reformer is an efficient Transformer model capable of handling context windows of about 1 million words with efficient memory usage. Developed by Google researchers Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya, it was presented at ICLR 2020. The Reformer introduces techniques such as Locality-Sensitive Hashing Attention, Chunked Feed Forward Layers, and Reversible Residual Layers to optimize memory requirements without significant performance loss. The article provides a step-by-step tutorial on using the Reformer in transformer models, including setting up the environment, installing libraries, and training the model. It concludes that advancements in transformers can lead to state-of-the-art technology in various domains.

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