Antonio Gulli, Sujit Pal

Deep Learning with Keras

Implementing deep learning models and neural networks with the power of Python

• Broschiertes Buch

Produktbeschreibung

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new second edition, updated for 2020 and featuring TensorFlow 2, the Keras API, CNNs, GANs, RNNs, NLP, and AutoML, has now been published. Key Features:Implement various deep learning algorithms in Keras and see how deep learning can be used in games See how various deep learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Book Description: This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks. What You Will Learn:Optimize step-by-step functions on a large neural network using the Backpropagation algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning Who this book is for: If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book.

Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 318
• Erscheinungstermin: 28. April 2017
• Englisch
• Abmessung: 235mm x 191mm x 18mm
• Gewicht: 597g
• ISBN-13: 9781787128422
• ISBN-10: 1787128423
• Artikelnr.: 48212277

Autorenporträt

Antonio Gulli is a transformational software executive and business leader with a passion for establishing and managing global technological talent for innovation and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and manage teams in six different countries in Europe and America. Currently, he works as site lead and director of cloud in Google Warsaw, driving European efforts for Serverless, Kubernetes, and Google Cloud UX. Previously, Antonio helped to innovate academic search as the vice president for Elsevier, a worldwide leading publisher. Before that, he drove query suggestions and news search as a principal engineer for Microsoft. Earlier, he served as the CTO for Ask.com, driving multimedia and news search. Antonio has filed for 20+ patents, published multiple academic papers, and served as a senior PC member in multiple international conferences. He truly believes that to be successful, you must have a great combination of management, research skills, just-get-it-done, and selling attitude.Amita Kapoor is an associate professor in the Department of Electronics, SRCASW, University of Delhi. She has been actively teaching neural networks for the last 20 years. She did her master's in electronics in 1996 and PhD in 2011. During her PhD, she was awarded the prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She had been awarded the best presentation award at International Conference Photonics 2008 for her paper. She is a member of professional bodies such as OSA (Optical Society of America), IEEE (Institute of Electrical and Electronics Engineers), INNS (International Neural Network Society), and ISBS (Indian Society for Buddhist Studies). Amita has more than 40 publications in international journals and conferences to her credit. Her present research areas include machine learning, artificial intelligence, neural networks, robotics, Buddhism (philosophy and psychology) and ethics in AI.