Machine Learning Algorithms Using Scikit And Tensorflow Environments
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Machine Learning Algorithms Using Scikit and TensorFlow Environments
Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Converging Pharmacy Science and Engineering in Computational Drug Discovery
Author: Tripathi, Rati Kailash Prasad
language: en
Publisher: IGI Global
Release Date: 2024-04-22
The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery. Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.