Re: Neural Networks Ebook Free Download
Evaristo Nicholls
Neural Networks and Deep Learning is a free online book. Thebook will teach you about:
- Neural networks, a beautiful biologically-inspired programmingparadigm which enables a computer to learn from observational data
- Deep learning, a powerful set of techniques for learning in neuralnetworks
James McCaffrey leads you through the fundamental concepts of neural networks, including their architecture, input-output, tanh and softmax activation, back-propagation, error and accuracy, normalization and encoding, and model interpretation. Although most concepts are relatively simple, there are many of them, and they interact with each other in unobvious ways, which is a major challenge of neural networks. But you can learn all important neural network concepts by running and examining the code in Neural Networks with JavaScript Succinctly, with complete example programs for the three major types of neural network problems.
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:
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.
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Should you spend time using deep learning models or can you use machine learning techniques to achieve the same results? Is it better to build a new neural network or use an existing pretrained network for image classification? What deep learning framework should you use?
This ebook by Holk Cruse provides an introduction to the simulation of dynamic systems in biology based on the combination of system theory, biological cybernetics, and the theory of neural networks. Mainly written as text book for students of biology, it is based on illustration rather than mathematical expressions. The third edition has been improved by a rearrangement and minor expansion of the sections referring to recurrent networks. These changes allow for an easy comprehension of the essential aspects of this important domain that has received growing attention during the last years.
For example, imagine (in an incredible simplification of typical neural nets used in practice) that we have just two weights w1 and w2. Then we might have a loss that as a function of w1 and w2 looks like this:
This ebook goes through the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. You will gain a deeper understanding of each architecture to help build your deep learning knowledge.
Neural Networks and deep learning - pretty much all you need to know about deep learning. In this book, you will start with the foundations of neural networks and its basic architecture and then move on to the intricacies of training a neural network, and more.
In this ebook, you will learn how to use neural nets to recognize handwritten digits, how the backpropagation algorithm works, improving the way neural networks learn, a visual proof that neural nets can compute any function, why deep neural networks are hard to retrain, and more foundations of deep learning.
This e-book not only gives you a great overview of deep learning, but it starts with the foundations and the depths of machine learning. It takes you on a journey by first understanding machine learning before getting into the application of neural networks and deep learning. A deep book that covers pretty much everything.
In this ebook, you will first start with understanding the foundations of the Neuron Model and Network Architectures and then further go into the perceptron learning rule. You will then get more theory around vector spaces, linear transformation, and more.
Created by Microsoft researchers Li Deng and Dong Yu, this ebook gives you an overview of the methodologies behind deep learning and their different types of applications. You will go further into speech and audio processing, NLP, information retrieval, and more.
This ebook is a case-based approach to understanding Deep Neural Networks. Chapters include computational graphs and TensorFlow, single neurons, feedforward neural networks, training neural networks, regularization, metric analysis, hyperparameter tuning, convolutional and recurrent neural networks, logistic regression from scratch, and a research project.
This ebook goes through the practical and comprehensive introduction of deep learning in the context of physical simulations. You are provided with hands-on code examples using Jupyter notebooks. Topics include physical losses, differentiable physics, NNS, reinforcement learning, and more.
This ebook has two sections, the first starting off with Math and the basics of machine learning and the second moving into deep neural networks. You will be able to transition from machine learning to deep learning and understand how they apply to one another. You also have access to exercises and lectures to cater to your study needs.
This ebook on GitHub has many chapters, but as we are focusing on deep learning I will attach the link for that - but feel free to have a gander at the other chapters available. In the deep learning chapter, you will understand more about neural networks and how the Jupyter notebook works. It focuses on the fastai deep learning library and how you can walk through it with them and get a more practical understanding of deep learning.
If you want to learn more about Mathematics and how it applies to Computer Science and Machine Learning - this is what you need to read. It is a deep ebook that goes through math in so much depth that once you finish this book, I'm convinced you will have graduated with a master's in Math.
Get the FREE ebook 'The Great Big Natural Language Processing Primer' and 'The Complete Collection of Data Science Cheat Sheets' along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.
Get the FREE ebook 'The Great Big Natural Language Processing Primer' and 'The Complete Collection of Data Science Cheat Sheets' along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression.The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME.In addition, the book presents methods specific to deep neural networks.
This book is my attempt to provide a brief but comprehensive introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs.
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