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Author: Madhuri Amit Sahu Publisher: Notion Press ISBN: Category : Education Languages : en Pages : 0
Book Description
Begin a fascinating trip through the depths of neural networks with "Mastering Neural Networks: From Basics to Advanced Deep Learning." This book, written by, Yugant Gotmare, Shreya Manapure, and Madhuri Sahu, is an essential resource for both beginners and seasoned experts who want to understand the complexities of AI. They follow the evolutionary path of machine intelligence, decoding the human brain model and studying the architecture of neural networks. Dive into supervised and unsupervised learning methods, including Hebbian theory and backpropagation. Learn more about advanced neural networks, including Kohonen Self-Organizing Feature Maps and the breakthrough uses of Optical Neural Networks. Face issues such as underfitting and overfitting in deep learning while learning about Convolutional Networks and their applications in cutting-edge industries such as Speech Recognition and Natural Language Processing. "Mastering Neural Networks: From Basics to Advanced Deep Learning" equips readers with the tools and confidence to navigate the ever-evolving landscape of artificial intelligence, fostering meaningful contributions to the field.
Author: Madhuri Amit Sahu Publisher: Notion Press ISBN: Category : Education Languages : en Pages : 0
Book Description
Begin a fascinating trip through the depths of neural networks with "Mastering Neural Networks: From Basics to Advanced Deep Learning." This book, written by, Yugant Gotmare, Shreya Manapure, and Madhuri Sahu, is an essential resource for both beginners and seasoned experts who want to understand the complexities of AI. They follow the evolutionary path of machine intelligence, decoding the human brain model and studying the architecture of neural networks. Dive into supervised and unsupervised learning methods, including Hebbian theory and backpropagation. Learn more about advanced neural networks, including Kohonen Self-Organizing Feature Maps and the breakthrough uses of Optical Neural Networks. Face issues such as underfitting and overfitting in deep learning while learning about Convolutional Networks and their applications in cutting-edge industries such as Speech Recognition and Natural Language Processing. "Mastering Neural Networks: From Basics to Advanced Deep Learning" equips readers with the tools and confidence to navigate the ever-evolving landscape of artificial intelligence, fostering meaningful contributions to the field.
Author: Dr.M.Kasthuri Publisher: SK Research Group of Companies ISBN: 9364922387 Category : Fiction Languages : en Pages : 228
Book Description
Dr.M.Kasthuri, Associate Professor, Department of Computer Science, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India. Mrs.K.Kalaiselvi, Guest Lecturer, Department of Computer Science, Thanthai Periyar Government Arts and Science College, Tiruchirappalli, Tamil Nadu, India.
Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 72
Book Description
This comprehensive guide dives into the fascinating world of Artificial Intelligence (AI) and its cutting-edge subfield, Generative AI. Designed for beginners and enthusiasts alike, it equips you with the knowledge and skills to navigate the complexities of machine learning and unlock the power of AI for advanced applications. Building a Strong Foundation The journey begins with mastering the fundamentals. You'll explore the different approaches to AI, delve into the history of this revolutionary field, and gain a solid understanding of various subfields like Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Delving into Machine Learning Machine learning, the core of AI's learning ability, takes center stage. You'll grasp the difference between supervised and unsupervised learning paradigms, discover popular algorithms like decision trees and neural networks, and learn the importance of data preparation for optimal model performance. Evaluation metrics become your tools to measure how effectively your models are learning. Unveiling the Power of Deep Learning Get ready to explore the intricate world of Deep Learning, a powerful subset of machine learning inspired by the human brain. Demystify neural networks, the building blocks of deep learning, and dive into specialized architectures like Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for handling sequential data. Deep learning frameworks become your allies, simplifying the process of building and deploying complex deep learning models. The Art of Machine Creation: Generative AI The book then shifts its focus to the transformative realm of Generative AI. Here, machines not only learn but create entirely new data. Explore different types of generative models, from autoregressive models to variational autoencoders, and witness their applications in text generation, image synthesis, and even music creation. A Deep Dive into Generative Adversarial Networks (GANs) Among generative models, Generative Adversarial Networks (GANs) have captured the imagination of researchers and the public alike. This chapter delves into the intriguing concept of GANs, where a generator model continuously strives to create realistic data while a discriminator model acts as a critic, ensuring the generated data is indistinguishable from real data. You'll explore the training process, the challenges of taming GANs, and best practices for achieving optimal results. Advanced Applications Across Domains The book then showcases the transformative potential of Generative AI across various domains. Witness the power of text generation with RNNs, explore the ethical considerations surrounding deepfakes, and discover how chatbots are revolutionizing communication. In the visual realm, delve into Deep Dream and Neural Style Transfer algorithms, and witness the creation of realistic images and videos with cutting-edge generative models. Mastering AI and Generative AI empowers you to not only understand these revolutionary technologies but also leverage them for advanced applications. As you embark on this journey, be prepared to unlock the boundless potential of machine creation and shape the future of AI.
Author: Ashish Ranjan Jha Publisher: Packt Publishing Ltd ISBN: 1789616409 Category : Computers Languages : en Pages : 450
Book Description
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures 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 and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch 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 and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Author: Graupe Daniel Publisher: World Scientific ISBN: 9811201242 Category : Computers Languages : en Pages : 440
Book Description
The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.
Author: Mark Graph Publisher: ISBN: 9781699947357 Category : Languages : en Pages : 235
Book Description
This book doesn't have any superpowers or magic formula to help you master the art of neural networks and deep learning. We believe that such learning is all in your heart. You need to learn a concept by heart and then brainstorm its different possibilities. I don't claim that after reading this book you will become an expert in Python and Deep Learning Neural Networks. Instead, you will, for sure, have a basic understanding of deep learning and its implications and real-life applications. Most of the time, what confuses us is the application of a certain thing in our lives. Once we know that, we can relate the subject to that particular thing and learn. An interesting thing is that neural networks also learn the same way. This makes it easier to learn about them when we know the basics. Let's take a look at what this book has to offer: ● The basics of Python including data types, operators and numbers. ● Advanced programming in Python with Python expressions, types and much more. ● A comprehensive overview of deep learning and its link to the smart systems that we are now building. ● An overview of how artificial neural networks work in real life. ● An overview of PyTorch. ● An overview of TensorFlow. ● An overview of Keras. ● How to create a convolutional neural network. ● A comprehensive understanding of deep learning applications and its ethical implications, including in the present and future. This book offers you the basic knowledge about Python and Deep Learning Neural Networks that you will need to lay the foundation for future studies. This book will start you on the road to mastering the art of deep learning neural networks. When I say that I don't have the magic formula to make you learn, I mean it. My point is that you should learn Python coding and Python libraries to build neural networks by practicing hard. The more you practice, the better it is for your skills. It is only after thorough and in depth practice that you will be able to create your own programs. Unlike other books, I don't claim that this book will make you a master of deep learning after a single read. That's not realistic, in fact, it's even a bit absurd. What I claim is that you will definitely learn about the basics. The rest is practice. The more you practice the better you code.
Author: Charu C. Aggarwal Publisher: Springer Nature ISBN: 3031296427 Category : Computers Languages : en Pages : 542
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. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories: The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2. Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections 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. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.
Author: Prasanjeet Sikder Publisher: Prasanjeet Sikder ISBN: Category : Computers Languages : en Pages : 174
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: Madhuri Amit Sahu
Author: Madhuri Amit Sahu
Author: Dr.M.Kasthuri
Author: Cybellium Ltd
Author: Anand Vemula
Author: Cybellium Ltd
Author: Ashish Ranjan Jha
Author: Graupe Daniel
Author: Mark Graph
Author: Charu C. Aggarwal
Author: Prasanjeet Sikder