Is Neural Networks And Deep Learning Same
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Neural Networks and Deep Learning
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.
DEEP LEARNING: NEURAL NETWORK AND BEYOND
Author: Dr. S. Suganya
language: en
Publisher: Xoffencer international book publication house
Release Date: 2024-07-05
Deep learning has brought about a revolution in the field of artificial intelligence by providing sophisticated tools that can be used to solve difficult issues in a variety of fields. One of the most important components of deep learning is the neural network, which is a computational model that is modeled after the structure and function of the human brain. Neural networks are made up of neurons, which are nodes that are connected to one another and are arranged in layers. Input data is processed by each neuron, and signals are then transmitted to neurons in the subsequent layer, which finally results in the production of output. The process of neural networks learning from data is referred to as backpropagation. This involves altering the strength of connections between neurons in order to reduce the amount of errors that occur in their predictions. However, the scope of deep learning encompasses a much wider range of applications than typical neural networks. In order to improve the capabilities of these models, researchers are continually investigating novel structures and methods. Examples of neural networks that are specifically developed for processing grid-like data include convolutional neural networks (CNNs), which are used to process images. Convolutional neural networks (CNNs) are able to effectively capture spatial hierarchies in visual input by utilizing convolutional layers. This enables CNNs to perform tasks such as image categorization and object detection with exceptional accuracy. The use of recurrent neural networks (RNNs) is another key innovation that is particularly well-suited for sequential data processing tasks. Some examples of these tasks include the understanding of natural language and the prediction of time series. In contrast to feedforward neural networks, recurrent neural networks (RNNs) feature connections that create directed cycles, which provide them with the ability to remember previous inputs. The ability of RNNs to record temporal connections in data is made possible by this memory, which makes them extremely useful for jobs that require context or continuity. In addition to these well-established designs, academics are investigating more unusual models such as transformers and generative adversarial networks (GANs). An artificial neural network (GAN) is made up of two neural networks—a generator and a discriminator—that are involved in a process of competitive learning. Because of this configuration, GANs are able to generate synthetic data that is realistic, which has a wide range of applications, including drug discovery and image synthesis.
Anatomy of Deep Learning Principles-Writing a Deep Learning Library from Scratch
This book introduces the basic principles and implementation process of deep learning in a simple way, and uses python's numpy library to build its own deep learning library from scratch instead of using existing deep learning libraries. On the basis of introducing basic knowledge of Python programming, calculus, and probability statistics, the core basic knowledge of deep learning such as regression model, neural network, convolutional neural network, recurrent neural network, and generative network is introduced in sequence according to the development of deep learning. While analyzing the principle in a simple way, it provides a detailed code implementation process. It is like not teaching you how to use weapons and mobile phones, but teaching you how to make weapons and mobile phones by yourself. This book is not a tutorial on the use of existing deep learning libraries, but an analysis of how to develop deep learning libraries from 0. This method of combining the principle from 0 with code implementation can enable readers to better understand the basic principles of deep learning and the design ideas of popular deep learning libraries.