Название: Deep Learning Using Python Автор: S. Lovelyn Rose, L. Ashok Kumar, D. Karthika Renuka Издательство: Wiley Год: 2020 Страниц: 356 Язык: английский Формат: epub Размер: 16.4 MB
Deep Learning is an area that has garnered a lot of attention in recent past. It is a subdivision of Machine Learning. Machine Learning is a type of algorithm in which the user gives the input and expected output, and expects the machine to learn (find) the algorithm (model) to produce the desired (expected) output. This is in stark contrast to programming wherein we give the input and the algorithm produces the output. Machine Learning tasks take a set of data that is termed as dataset. The Machine Learning algorithm finds the patterns and inferences that can be made on the dataset.
Deep Learning, which was a science proposed in the 1980s, has become a technology now. Deep Learning has become a choice for any Artificial Intelligence (AI) problem because of its ability to learn well. It can be used in any application, ranging from simple applications such as image classification to complex real-time applications such as autonomous driving. Many Silicon Valley tech giants such as Google, Microsoft, Amazon and Facebook have been backing up Deep Learning since they have found extensive usage of neural networks in their products and solutions. Data analytics has a lot to do with these improvements.
Learning the concepts of Deep Learning is not enough to handle real-time cases. Deep Learning Using Python aims at giving a hands-on experience on common Deep Learning applications using popular frameworks of the most suitable language, Python. There is no need for a reader to be a professional programmer. All the codes are simple and the exhaustive comments will help readers to understand of the available code. The readers will be able to solve AI problems using neural networks and Deep Learning architectures.
The book has been divided into seven chapters. Chapter 1 elaborately deals with the fundamentals of Deep Learning, to enable any reader to understand the deep learning architectures elaborated in subsequent chapters. Chapter 2 deals with Convolutional Neural Networks (CNNs), which have proven to be very effective in the area of computer vision. Chapter 3 deals with Recurrent Neural Networks (RNNs) and its variants. The various types of autoencoders, which are a type of Artificial Neural Network used to learn efficient data encoding, are presented in Chapter 4. To learn the probability distribution over the set of inputs, Restricted Boltzmann Machine (RBM) is discussed in Chapter 5. Chapter 6 presents popular open source frameworks in Python for Deep Learning applications. Chapter 7 describes how to utilize the knowledge that you have gained from previous chapters in real-time applications.
For the reader to test his understanding of the chapters, review questions are given at the end of every chapter. To improve the thinking process and understanding, assignment problems of the apply-and-analyze type are also provided for every chapter.
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Deep Learning Using Python 
