Neural Computing Learning Solutions
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Neural Computing Learning Solutions
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Author : Deloitte & Touche
language : en
Publisher:
Release Date : 1996
Neural Computing Learning Solutions written by Deloitte & Touche and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Human engineering categories.
Neural Computing Learning Solutions
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Author : DTI Neural Computing Technology Transfer Programme
language : en
Publisher:
Release Date : 1994
Neural Computing Learning Solutions written by DTI Neural Computing Technology Transfer Programme and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Bionics categories.
Neural Computing Learning Solutions
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Author :
language : en
Publisher:
Release Date : 1994
Neural Computing Learning Solutions written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.
Neural Computing Learning Solutions
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Author : Touche Ross & Co. (Management Consultants)
language : en
Publisher:
Release Date : 1993
Neural Computing Learning Solutions written by Touche Ross & Co. (Management Consultants) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with categories.
Neural Computing
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Author : Great Britain. Department of Trade and Industry
language : en
Publisher:
Release Date : 1994
Neural Computing written by Great Britain. Department of Trade and Industry and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Neural computers categories.
Applied Machine Learning Solutions With Python
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Author : Siddhanta Bhatta
language : en
Publisher: BPB Publications
Release Date : 2021-08-31
Applied Machine Learning Solutions With Python written by Siddhanta Bhatta and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts. KEY FEATURES ● Popular techniques for problem formulation, data collection, and data cleaning in machine learning. ● Comprehensive and useful machine learning tools such as MLFlow, Streamlit, and many more. ● Covers numerous machine learning libraries, including Tensorflow, FastAI, Scikit-Learn, Pandas, and Numpy. DESCRIPTION This book discusses how to apply machine learning to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine learning works through numerous examples and case studies. The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will learn how to formulate a problem, collect data, build a model, and tune it. You will learn about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine learning profile, various machine learning libraries, Statistics, and FAST API. Throughout the book, you will use Python to experiment with machine learning libraries such as Tensorflow, Scikit-learn, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets. WHAT YOU WILL LEARN ● Construct a machine learning problem, evaluate the feasibility, and gather and clean data. ● Learn to explore data first, select, and train machine learning models. ● Fine-tune the chosen model, deploy, and monitor it in production. ● Discover popular models for data analytics, computer vision, and Natural Language Processing. ● Create a machine learning profile and contribute to the community. WHO THIS BOOK IS FOR This book caters to beginners in machine learning, software engineers, and students who want to gain a good understanding of machine learning concepts and create production-ready ML systems. This book assumes you have a beginner-level understanding of Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Problem Formulation in Machine Learning 3. Data Acquisition and Cleaning 4. Exploratory Data Analysis 5. Model Building and Tuning 6. Taking Our Model into Production 7. Data Analytics Use Case 8. Building a Custom Image Classifier from Scratch 9. Building a News Summarization App Using Transformers 10. Multiple Inputs and Multiple Output Models 11. Contributing to the Community 12. Creating Your Project 13. Crash Course in Numpy, Matplotlib, and Pandas 14. Crash Course in Linear Algebra and Statistics 15. Crash Course in FastAPI
Neural Networks And Computing
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Author : Tommy W. S. Chow
language : en
Publisher: Imperial College Press
Release Date : 2007
Neural Networks And Computing written by Tommy W. S. Chow and has been published by Imperial College Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area. Additionally, the book highlights the important feature selection problem, which baffles many neural networks practitioners because of the difficulties handling large datasets. It also contains several interesting IT, engineering and bioinformatics applications.
Applied Deep Learning With Pytorch
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Author : Hyatt Saleh
language : en
Publisher:
Release Date : 2019-04-26
Applied Deep Learning With Pytorch written by Hyatt Saleh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-26 with Computers categories.
Implement techniques such as image classification and natural language processing (NLP) by understanding the different neural network architectures Key Features Understand deep learning and how it can solve complex real-world problems Apply deep learning for image classification and text processing using neural networks Develop deep learning solutions for tasks such as basic classification and solving style transfer problems Book Description Machine learning is rapidly becoming the most preferred way of solving data problems, thanks to the huge variety of mathematical algorithms that find patterns, which are otherwise invisible to us. Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The book begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you'll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN). By the end of this book, you'll be able to apply the skills and confidence you've gathered along your learning process to use PyTorch for building deep learning solutions that can solve your business data problems. What you will learn Detect a variety of data problems to which you can apply deep learning solutions Learn the PyTorch syntax and build a single-layer neural network with it Build a deep neural network to solve a classification problem Develop a style transfer model Implement data augmentation and retrain your model Build a system for text processing using a recurrent neural network Who this book is for Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this book useful. Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.
Programming With Tensorflow
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Author : Kolla Bhanu Prakash
language : en
Publisher: Springer Nature
Release Date : 2021-01-22
Programming With Tensorflow written by Kolla Bhanu Prakash and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-22 with Technology & Engineering categories.
This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).
Deep Learning And Edge Computing Solutions For High Performance Computing
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Author : A. Suresh
language : en
Publisher: Springer Nature
Release Date : 2021-01-27
Deep Learning And Edge Computing Solutions For High Performance Computing written by A. Suresh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-27 with Technology & Engineering categories.
This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.