DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the platform, we demonstrate how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera. It is trained in simulation with no additional tuning in physical world and demonstrates: 1) formulation and solution of a robust reinforcement learning algorithm, 2) narrowing the reality gap through joint perception and dynamics, 3) distributed on-demand compute architecture for training optimal policies, and 4) a robust evaluation method to identify when to stop training. It is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning. We open source our code and video demo on GitHub 2 .

Machine Learning (ML) has become a mature technology that is being applied to a wide range of business problems such as web search, online advertising, product recommendations, object recognition, and so on. As a result, it has become imperative for researchers and practitioners to have a fundamental understanding of ML concepts and practical knowledge of end-to-end modeling. This tutorial takes a hands-on approach to introducing the audience to machine learning. The first part of the tutorial gives a broad overview and discusses some of the key concepts within machine learning. The second part of the tutorial takes the audience through the end-to-end modeling pipeline for a real-world income prediction problem.

No abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious and pathogenic virus. To date, there is a lack of proper medication against this virus, which has triggered the scientific community to find therapeutics. Searching of SARS-CoV-2 main protease inhibitors from anti-viral natural products based on traditional knowledge may be an effective approach. In this work, structure-based virtual screening of the compounds of Justicia adhatoda was performed against SARS-CoV-2 Mpro, followed by ADME filtration, molecular dynamics, and MMGBSA-based binding free energy calculation. On the basis of docking score, crucial interacting amino acid residues, molecular dynamics, and binding energy profile, three novel phenolic compounds JA_38b, JA_38c, and JA_39 were selected as potential binders against SARS-CoV-2 Mpro. This information may be used to develop potential therapeutics countermeasures against SARS-CoV-2 infection after in vitro and detailed pharmacological study.

No abstract