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Zustand: acceptable. This copy has clearly been enjoyedâ"expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.

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Soft cover. Zustand: Very Good. 3rd Edition. Black binding with red and white lettering on spine and front cover. Bottom corner tip of front cover slightly creased . Photos of book on rewquest.

paperback. Zustand: Good. minor stain to cover. softcover book in very good shape, very light wear to cover and book edges.

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paperback. Zustand: Good. Our good condition books are generally good for reading but not for gifting or collecting. They could have imperfections such as creasing, fanning, inscriptions, margin notes, yellowing, staining on edge or cover or pages, bumps, scuffs, etc etc (sometimes multiple of these). It's a wide category that encompasses anything that isn't almost-new down to anything that is slightly better than poor. We would NOT recommend gifting Good books - these should be considered reading copies. Our books are dispatched from a Yorkshire former cotton mill. We list via barcode/ISBN so please note that the images are stock images and may not be the exact copy you receive, furthermore the details about edition and year might not be accurate as many publishers reuse the same ISBN for multiple editions and as we simply scan a barcode or enter an ISBN we do not check the validity of the edition data when listing. If you're looking for an exact edition please don't order (at least not without checking with us first, although we don't always have time to check). We aim to dispatch prompty, the service used will depend on order value and book size. We can ship to most countries, see our shipping policies. Payment is via Abe only.…

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Zustand: New.

Zustand: New.

Paperback. Zustand: New. Leverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learningWhat you will learnInstall and configure Keras in TensorFlowMaster neural network programming using the Keras libraryUnderstand the different Keras layersUse Keras to implement simple feed-forward neural networks, CNNs and RNNsWork with various datasets and models used for image and text classificationDevelop text summarization and reinforcement learning models using KerasWho this book is forKeras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.…

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Paperback. Zustand: New.

Zustand: New.

Digital. Zustand: New. Get to grips with the basics of Keras to implement fast and efficient deep-learning modelsAbout This Book. 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 KerasWho This Book Is ForIf 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.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 learningIn DetailThis 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 hand written 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 (GAN). 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.Style and approachThis book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.…

Zustand: New.

Zustand: New. pp. 646 2nd Edition.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.