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Deep Learning With Keras Antonio Gulli

 
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MessagePosté le: Jeu 6 Juil - 09:22 (2017)    Sujet du message: Deep Learning With Keras Antonio Gulli Répondre en citant


Deep Learning with Keras
by Antonio Gulli



>>>DOWNLOAD BOOK Deep Learning with Keras


<h4>Key Features</h4><ul><li>Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games</li><li>See how various deep-learning models and practical use-cases can be implemented using Keras</li><li>A practical, hands-on guide with real-world examples to give you a strong foundation in Keras</li></ul><h4>Book Description</h4>
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 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.
<h4>What you will learn</h4><ul><li>Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm</li><li>Fine-tune a neural network to improve the quality of results</li><li>Use deep learning for image and audio processing</li><li>Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases</li><li>Identify problems for which Recurrent Neural Network (RNN) solutions are suitable</li><li>Explore the process required to implement Autoencoders</li><li>Evolve a deep neural network using reinforcement learning</li></ul><h4>About the Author</h4>
Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, 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 managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields spanning from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&amp;D (Microsoft and Google).

Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.
<h4>Table of Contents</h4><ol><li>Neural Networks Foundations</li><li>Keras Installation and API</li><li>Deep Learning with ConvNets</li><li>Generative Adversarial Networks and WaveNet</li><li>Word Embeddings</li><li>Recurrent Neural Network &#x2014; RNN</li><li>Additional Deep Learning Models</li><li>AI Game Playing</li><li>Conclusion</li></ol>







Deep Learning with Keras free iphone





The validation score for the model is then an average of the K validation scores obtainedFor regression problems, its very common to take the Mean Absolute Error (MAE) as a metric#for computing Probabilities of classes-"logit(log probabilities)ThanksDont worry if you dont get this entirely just now, youll read more about it later on! The units actually represents the kernel of the above formula or the weights matrix, composed of all weights given to all input nodes, created by the layerSome of the most basic ones are listed belowThis will require some additional preprocessingTable of Contents Chapter 1: Neural Networks Foundations Perceptron Multilayer perceptron the first example of a network A real example recognizing handwritten digits A practical overview of backpropagation Towards a deep learning approach Summary Chapter 2: Keras Installation and API Installing Keras Configuring Keras Installing Keras on Docker Installing Keras on Google Cloud ML Installing Keras on Amazon AWS Installing Keras on Microsoft Azure Keras API Callbacks for customizing the training process Summary Chapter 3: Deep Learning with ConvNets Deep convolutional neural network DCNN An example of DCNN LeNet Recognizing CIFAR-10 images with deep learning Very deep convolutional networks for large-scale image recognition Summary Chapter 4: Generative Adversarial Networks and WaveNet What is a GAN? Deep convolutional generative adversarial networks Keras adversarial GANs for forging MNIST Keras adversarial GANs for forging CIFAR WaveNet a generative model for learning how to produce audio Summary Chapter 5: Word Embeddings Distributed representations word2vec Exploring GloVe Using pre-trained embeddings Summary Chapter 6: Recurrent Neural Network RNN SimpleRNN cells RNN topologies Vanishing and exploding gradients Long short term memory LSTM Gated recurrent unit GRU Bidirectional RNNs Stateful RNNs Other RNN variants Summary Chapter 7: Additional Deep Learning Models Keras functional API Regression networks Unsupervised learning autoencoders Composing deep networks Customizing Keras Generative models Summary Chapter 8: AI Game Playing Reinforcement learning Example - Keras deep Q-network for catch The road ahead Summary Chapter 9: Conclusion Keras 2.0 what is new Book Details ISBN 139781787128422 Paperback318 pages You can visually compare the predictions with the actual test labels (ytest), or you can use all types of metrics to determine the actual performanceFor that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python

If you like to train neural networks with less code than in Keras, the only viable option is to use pigeonsPerceptrons The most simple neural network is the perceptron, which, in its simplest form, consists of a single neuron#Defining a Pooling layer which reduces the dimentions of the #features map and reduces the computational complexity of the modellayermaxpooling2d(poolsize=c(2,2)) %>% In this case, the result is stored in ypred: ypred = model.predict(Xtest) Before you go and evaluate your model, you can already get a quick idea of the accuracy by checking how ypred and ytest compare: ypred[:5] array([[0], [1], [0], [0], [0]], dtype=int32) ytest[:5] array([0, 1, 0, 0, 0]) You see that these values seem to add up, but what is all of this without some hard numbers? Evaluate Model Now that you have built your model and used it to make predictions on data that your model hadnt seen yet, its time to evaluate its performanceIf this was your first Deep Learning model in R like me, I hope you guys liked and enjoyed it 07f867cfac


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