Advanced Deep Learning For Engineers And Scientists
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Advanced Deep Learning For Engineers And Scientists
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Author : Kolla Bhanu Prakash
language : en
Publisher: Springer Nature
Release Date : 2021-07-24
Advanced Deep Learning For Engineers And Scientists 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 2021-07-24 with Technology & Engineering categories.
This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.
Machine Learning
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Author : Andreas Lindholm
language : en
Publisher:
Release Date : 2022
Machine Learning written by Andreas Lindholm and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Machine learning categories.
"This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning"--
Advanced Interdisciplinary Applications Of Deep Learning For Data Science
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Author : Mathew Biju, Soly
language : en
Publisher: IGI Global
Release Date : 2024-12-18
Advanced Interdisciplinary Applications Of Deep Learning For Data Science written by Mathew Biju, Soly and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-18 with Computers categories.
Advanced interdisciplinary applications of deep learning are revolutionizing the field of data science, enabling new solutions across industries. By leveraging neural networks and datasets, deep learning techniques are transforming how businesses analyze, interpret, and gain insights from complex data. In fields like healthcare, finance, engineering, and business, deep learning models can uncover patterns and make predictions to enhance data-driven decision-making while fostering new opportunities in areas like personalized medicine, autonomous systems, and predictive analytics. As deep learning continues evolves, its interdisciplinary impact expands, creating new possibilities for solving challenges in data science. Advanced Interdisciplinary Applications of Deep Learning for Data Science explores innovations of deep learning technology in data science for various industries and sectors. It examines solutions for decision-making and patient or customer service using machine learning, AI, and predictive analytics. This book covers topics such as bioinformatics, environmental monitoring, and medical diseases, and is a useful resource for data scientists, computer engineers, medical professionals, academicians, and researchers.
Mastering Pytorch
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Author : Ashish Ranjan Jha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31
Mastering Pytorch written by Ashish Ranjan Jha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Understand how to use PyTorch to build advanced neural network models Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text, vision, and music generation models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobile devices (Android and iOS) Become well versed in rapid prototyping using PyTorch with fastai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Design ResNets, LSTMs, and graph neural networks (GNNs) Create language and vision transformer models using Hugging Face Who this book is for This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
Hands On Transfer Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Deep Learning With Pytorch
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Author : Vishnu Subramanian
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-23
Deep Learning With Pytorch written by Vishnu Subramanian and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-23 with Computers categories.
Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book Description Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is for This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.
The Software Encyclopedia 2000
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Author : Bowker Editorial Staff
language : en
Publisher:
Release Date : 2000-05
The Software Encyclopedia 2000 written by Bowker Editorial Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-05 with Computers categories.
Feature Engineering For Modern Machine Learning With Scikit Learn
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Author : Cuantum Technologies
language : en
Publisher: Staten House
Release Date : 2024-11-06
Feature Engineering For Modern Machine Learning With Scikit Learn written by Cuantum Technologies and has been published by Staten House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-06 with Computers categories.
This Book grants Free Access to our e-learning Platform, which includes: ✅ Free Repository Code with all code blocks used in this book ✅ Access to Free Chapters of all our library of programming published books ✅ Free premium customer support ✅ Much more... Unleash the Power of Feature Engineering for Cutting-Edge Machine Learning Transform raw data into powerful features with Feature Engineering for Modern Machine Learning with Scikit-Learn: Advanced Data Science and Practical Applications. This essential guide takes you beyond the basics, teaching you how to create, optimize, and automate features that elevate machine learning models. With a focus on real-world applications and advanced techniques, this book equips data scientists, machine learning engineers, and analytics professionals with the skills to make impactful, data-driven decisions. Why Advanced Feature Engineering is Essential In machine learning, the quality of input data determines the quality of output predictions. Advanced feature engineering is the key to uncovering hidden patterns and meaningful insights in your data, transforming it into structured inputs that drive model performance. This book provides a deep dive into creating and refining features tailored to your data's unique challenges, ensuring models are both accurate and insightful. What You'll Discover Inside Feature Engineering for Modern Machine Learning with Scikit-Learn covers every stage of advanced feature engineering, from foundational transformations to automated pipelines and cutting-edge tools: Automating Data Preparation with Scikit-Learn Pipelines: Learn to create reproducible, automated workflows that handle everything from scaling and encoding to feature selection. Advanced Feature Creation and Transformation: Master complex techniques like polynomial features, interaction terms, and dimensionality reduction, all designed to improve model accuracy. Industry-Specific Case Studies: Apply feature engineering techniques to real-world domains like healthcare, retail, and customer segmentation, gaining insights into how feature engineering adapts across fields. Modern Tools and Automation with AutoML: Explore AutoML tools like TPOT and Auto-sklearn to automate feature selection and model optimization, allowing you to focus on the highest-impact features. Deep Learning Feature Engineering: Discover techniques tailored for neural networks, including data augmentation, embeddings, and feature transformations that enhance deep learning workflows. Who Should Read This Book Whether you're an experienced data scientist or an advanced beginner looking to build cutting-edge skills, this book provides essential techniques for modern machine learning. It's ideal for anyone who wants to: Maximize model performance through impactful feature engineering. Build efficient, reproducible workflows with Scikit-Learn. Explore advanced applications across multiple domains. Elevate Your Models with Advanced Feature Engineering Feature Engineering for Modern Machine Learning with Scikit-Learn is more than just a guide-it's a toolkit for creating the data transformations that drive high-performing models. Equip yourself with the latest techniques, tools, and insights to confidently tackle real-world data science challenges and unlock the full potential of your machine learning projects. Dive into the world of feature engineering and elevate your data science expertise today!
Advanced Deep Learning With Python
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Author : Ivan Vasilev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-12
Advanced Deep Learning With Python written by Ivan Vasilev and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-12 with Computers categories.
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
A Greater Foundation For Machine Learning Engineering
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Author : Dr Ganapathi Pulipaka
language : en
Publisher: Xlibris Us
Release Date : 2021-10
A Greater Foundation For Machine Learning Engineering written by Dr Ganapathi Pulipaka and has been published by Xlibris Us this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10 with categories.
The book provides foundations of machine learning and algorithms with a road map to deep learning, genesis of machine learning, installation of Python, supervised machine learning algorithms and implementations in Python or R, unsupervised machine learning algorithms in Python or R including natural language processing techniques and algorithms, Bayesian statistics, origins of deep learning, neural networks, and all the deep learning algorithms with some implementations in TensorFlow and architectures, installation of TensorFlow, neural net implementations in TensorFlow, Amazon ecosystem for machine learning, swarm intelligence, machine learning algorithms, in-memory computing, genetic algorithms, real-world research projects with supercomputers, deep learning frameworks with Intel deep learning platform, Nvidia deep learning frameworks, IBM PowerAI deep learning frameworks, H2O AI deep learning framework, HPC with deep learning frameworks, GPUs and CPUs, memory architectures, history of supercomputing, infrastructure for supercomputing, installation of Hadoop on Linux operating system, design considerations, e-Therapeutics's big data project, infrastructure for in-memory data fabric Hadoop, healthcare and best practices for data strategies, R, architectures, NoSQL databases, HPC with parallel computing, MPI for data science and HPC, and JupyterLab for HPC.