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
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.
Who is this book 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.
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
This is definitely one of the best resources if you want to learn Keras.
Amazon Verified review
Arbaaz QureshiJun 23, 2018
5
Good book, for readers who are familiar with deep learning concepts and Keras and wish to improve them further. Correct the errors of the code section, in the next version.
Amazon Verified review
Tae S. ShinDec 09, 2018
5
I needed a reference book to use Keras that is a user-oriented library for easy modeling of neural networks in Python. Unlike some low reviews on the book, it turned out to be exactly what I expected and what its title said, Implementing deep learning models and neural networks with Keras in Python.If you want to know more about theory of deep learning, you should refer to other deep learning books. If you want to know how Keras API internally works, you may want to look at other books on Tensorflow or Theano that was low level API for Keras and with which you can define neural networks in node-level. But if you want to flexibly and easily build a NN model with fewer lines of code, this book might be good for you.
Amazon Verified review
MelvinDec 30, 2017
5
I felt compelled to write a review because I really think this is an exceptionally good book under the circumstances. When I say "under the circumstances", I mean given the fact that deep learning is a challenging topic to explain and requires both a theoretic and practical approach to be appreciated. The author clearly avoids getting bogged down with the theoretical aspects and I can appreciate why since a thorough theoretical understanding would require a separate book in it's own right.This book will not help you understand the theory or underlying mathematics. However, if you already understand the theory and want to learn to use a package like Keras then this is the book for you.This book stands out because it gives details about the implementation aspects of coding many different deep learning models that you will hear about in the literature and in the field. For example, LeNet, ResNet, etc. among many others are demonstrated through out the book.Generally speaking, topics in deep learning are not easy to explain to the average reader and I think the author recognizes this difficulty and chooses to place his focus on demonstrating how to implement deep learning methods and being careful to explain what the different modules do and their respective parameters.In my view, this book is very suitable for Data Scientists who already know the spectrum of machine learning models and techniques and want to get their hands dirty as fast as possible with deep learning. This book is a much better practical book for deep learning than the popular book by Aurélien Géron called "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems". I have looked at many deep learning books and in my view this one did the best job is getting me comfortable with implementing deep learning models on my own.The one thing that I found the book was lacking is that it's final chapter on AI and reinforcement learning did not seem as thorough and detailed as the other chapters in the book. Having reviewed many books in the area of deep learning, I can honestly say this is probably the best book I have come across so far. However, I came to this book already having a solid understand of deep learning theory.
Antonio Gulli是企业领导和软件部门高管,极具创新精神和执行力,并乐于发现和管理全球高科技人才。他是搜索引擎、在线服务、机器学习、信息检索、数据分析以及云计算等多方面的专家。他幸运地拥有欧洲4个不同国家的工作经验,并管理过来自欧洲和美国6个不同国家的员工。Antonio在出版业(Elsevier)、消费者互联网(Ask.com 和Tiscali)以及高科技研发(微软和谷歌)等多个跨度的行业里历任CEO、GM、CTO、副总裁、总监及区域主管。
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