Neural Networks Unleashed: The Power of Deep Learning PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Neural Networks Unleashed: The Power of Deep Learning PDF full book. Access full book title Neural Networks Unleashed: The Power of Deep Learning by Mrs.K.T.Caroline Gnanatheepa. Download full books in PDF and EPUB format.
Author: Mrs.K.T.Caroline Gnanatheepa
Publisher: SK Research Group of Companies
ISBN: 9364927508
Category : Computers
Languages : en
Pages : 39
Get Book Here
Book Description
Mrs.K.T.Caroline Gnanatheepa, Assistant Professor, Department of Computer Science, S.I.V.E.T. College, University of Madras, Chennai, Tamil Nadu, India.
Author: Mrs.K.T.Caroline Gnanatheepa
Publisher: SK Research Group of Companies
ISBN: 9364927508
Category : Computers
Languages : en
Pages : 39
Get Book Here
Book Description
Mrs.K.T.Caroline Gnanatheepa, Assistant Professor, Department of Computer Science, S.I.V.E.T. College, University of Madras, Chennai, Tamil Nadu, India.
Author: Dino Quintero
Publisher: IBM Redbooks
ISBN: 0738442941
Category : Computers
Languages : en
Pages : 278
Get Book Here
Book Description
This IBM® Redbooks® publication is a guide about the IBM PowerAI Deep Learning solution. This book provides an introduction to artificial intelligence (AI) and deep learning (DL), IBM PowerAI, and components of IBM PowerAI, deploying IBM PowerAI, guidelines for working with data and creating models, an introduction to IBM SpectrumTM Conductor Deep Learning Impact (DLI), and case scenarios. IBM PowerAI started as a package of software distributions of many of the major DL software frameworks for model training, such as TensorFlow, Caffe, Torch, Theano, and the associated libraries, such as CUDA Deep Neural Network (cuDNN). The IBM PowerAI software is optimized for performance by using the IBM Power SystemsTM servers that are integrated with NVLink. The AI stack foundation starts with servers with accelerators. graphical processing unit (GPU) accelerators are well-suited for the compute-intensive nature of DL training, and servers with the highest CPU to GPU bandwidth, such as IBM Power Systems servers, enable the high-performance data transfer that is required for larger and more complex DL models. This publication targets technical readers, including developers, IT specialists, systems architects, brand specialist, sales team, and anyone looking for a guide about how to understand the IBM PowerAI Deep Learning architecture, framework configuration, application and workload configuration, and user infrastructure.
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319944630
Category : Computers
Languages : en
Pages : 512
Get Book Here
Book Description
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 221
Get Book Here
Book Description
Unleash the Power of Deep Learning for Intelligent Systems In the realm of artificial intelligence and machine learning, neural networks stand as the driving force behind intelligent systems that mimic human cognition. "Mastering Neural Networks" is your ultimate guide to comprehending and harnessing the potential of these powerful algorithms, empowering you to create intelligent solutions that push the boundaries of innovation. About the Book: As technology advances, the capabilities of neural networks become more integral to various fields. "Mastering Neural Networks" offers an in-depth exploration of this cutting-edge subject—an essential toolkit for data scientists, engineers, and enthusiasts. This book caters to both newcomers and experienced learners aiming to excel in neural network concepts, architectures, and applications. Key Features: Neural Network Fundamentals: Begin by understanding the core principles of neural networks. Learn about artificial neurons, activation functions, and the architecture of these powerful algorithms. Feedforward Neural Networks: Dive into feedforward neural networks. Explore techniques for designing, training, and optimizing networks for various tasks. Convolutional Neural Networks: Grasp the art of convolutional neural networks. Understand how these architectures excel in image and pattern recognition tasks. Recurrent Neural Networks: Explore recurrent neural networks. Learn how to process sequences and time-series data, making them suitable for tasks like language modeling and speech recognition. Generative Adversarial Networks: Understand the significance of generative adversarial networks. Explore how these networks enable the generation of realistic images, text, and data. Transfer Learning and Fine-Tuning: Delve into transfer learning. Learn how to leverage pretrained models and adapt them to new tasks, saving time and resources. Neural Network Optimization: Grasp optimization techniques. Explore methods for improving network performance, reducing overfitting, and tuning hyperparameters. Real-World Applications: Gain insights into how neural networks are applied across industries. From healthcare to finance, discover the diverse applications of these algorithms. Why This Book Matters: In a world driven by intelligent systems, mastering neural networks offers a competitive advantage. "Mastering Neural Networks" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge algorithms, enabling them to create intelligent solutions that redefine the boundaries of innovation. Unleash the Future of Intelligence: In the landscape of artificial intelligence, neural networks are reshaping technology and innovation. "Mastering Neural Networks" equips you with the knowledge needed to leverage these powerful algorithms, enabling you to create intelligent solutions that push the boundaries of innovation and redefine what's possible. Whether you're a seasoned practitioner or new to the world of neural networks, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering neural networks starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Author: Dipanjan Sarkar
Publisher: Packt Publishing Ltd
ISBN: 1788839056
Category : Computers
Languages : en
Pages : 430
Get Book Here
Book Description
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.
Author: Sun, Zhaohao
Publisher: IGI Global
ISBN: 179989018X
Category : Computers
Languages : en
Pages : 425
Get Book Here
Book Description
Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students.
Author: Cao Xiao
Publisher: Springer Nature
ISBN: 3030821846
Category : Medical
Languages : en
Pages : 236
Get Book Here
Book Description
This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.
Author: Prasanjeet Sikder
Publisher: Prasanjeet Sikder
ISBN:
Category : Computers
Languages : en
Pages : 174
Get Book Here
Book Description
**Title: Mastering Deep Learning with Keras: From Fundamentals to Advanced Techniques** **Chapter 1: Introduction to Deep Learning and Keras** - Understanding the basics of deep learning - Introducing the Keras framework - Setting up your development environment **Chapter 2: Building Blocks of Neural Networks** - Exploring layers and activations - Creating various types of layers using Keras - Initializing weights and biases **Chapter 3: Building Your First Neural Network with Keras** - Creating a simple feedforward neural network - Compiling the model with loss functions and optimizers - Training the model and monitoring progress **Chapter 4: Convolutional Neural Networks (CNNs)** - Understanding CNN architecture - Implementing image recognition using Keras - Transfer learning with pre-trained CNN models **Chapter 5: Recurrent Neural Networks (RNNs)** - Introduction to sequential data processing - Building and training RNNs using Keras - Applications of RNNs in natural language processing and time series analysis **Chapter 6: Advanced Keras Functionalities** - Callbacks for model customization and monitoring - Handling overfitting with regularization techniques - Custom layers and loss functions **Chapter 7: Deep Learning for Natural Language Processing** - Text preprocessing and tokenization - Building text classification and sentiment analysis models - Sequence-to-sequence models for machine translation **Chapter 8: Deep Learning for Computer Vision** - Object detection and localization using Keras - Generating images with Generative Adversarial Networks (GANs) - Image segmentation with U-Net architecture **Chapter 9: Deployment and Productionization** - Exporting Keras models for production - Integration with web frameworks and APIs - Converting models to optimized formats (TensorFlow Lite, ONNX) **Chapter 10: Cutting-Edge Deep Learning Techniques** - Introduction to attention mechanisms - Exploring Transformers and BERT models - Reinforcement learning with Keras **Chapter 11: Case Studies and Real-World Projects** - Deep learning applications in various industries - Walkthroughs of projects using Keras for specific tasks - Best practices and lessons learned from real projects **Chapter 12: The Future of Keras and Deep Learning** - Emerging trends in deep learning - Keras updates and upcoming features - Ethical considerations and responsible AI in deep learning **Appendix: Keras Cheat Sheet** - Quick reference guide to Keras syntax, functions, and methods **Appendix: Keras Interview Questions with Answers**
Author: Shekhar Khandelwal
Publisher: BPB Publications
ISBN: 9355515391
Category : Computers
Languages : en
Pages : 251
Get Book Here
Book Description
A hands-on guide to building and deploying deep learning models with Python KEY FEATURES ● Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning tasks. ● Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). ● Gain hands-on experience by working on practical projects and applying deep learning techniques to real-world problems. DESCRIPTION “Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and deep learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and deep learning. The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations. By the end of the book, you will be able to use deep learning to solve real-world problems. WHAT YOU WILL LEARN ● Develop a comprehensive understanding of neural networks' key concepts and principles. ● Gain proficiency in Python as you code and implement major deep-learning algorithms from scratch. ● Build and implement predictive models using various neural networks ● Learn how to use Transformers for complex NLP tasks ● Explore techniques to enhance the performance of your deep learning models. WHO THIS BOOK IS FOR This book is for anyone who is interested in a career in emerging technologies, such as artificial intelligence (AI), data analytics, machine learning, deep learning, and data science. It is a comprehensive guide that covers the fundamentals of these technologies, as well as the skills and knowledge that you need to succeed in this field. TABLE OF CONTENTS 1. Python for Data Science 2. Real-World Challenges for Data Professionals in Converting Data Into Insights 3. Build a Neural Network-Based Predictive Model 4. Convolutional Neural Networks 5. Optical Character Recognition 6. Object Detection 7. Image Segmentation 8. Recurrent Neural Networks 9. Generative Adversarial Networks 10. Transformers
Author: Raghav Bali
Publisher: Packt Publishing Ltd
ISBN: 1787128288
Category : Computers
Languages : en
Pages : 1123
Get Book Here
Book Description
Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.
