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|a HCDD
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1 |
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|a Zaccone, Giancarlo.
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| 245 |
1 |
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|a Deep Learning with TensorFlow :
|b Explore neural networks and build intelligent systems with Python, 2nd Edition.
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| 250 |
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|a 2nd ed.
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| 260 |
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|a Birmingham :
|b Packt Publishing,
|c 2018.
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| 300 |
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|a 1 online resource (483 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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0 |
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|a Print version record.
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|a Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; A soft introduction to machine learning; Supervised learning; Unbalanced data; Unsupervised learning; Reinforcement learning; What is deep learning?; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs).
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| 505 |
8 |
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|a Convolutional Neural Networks (CNNs)AutoEncoders; Recurrent Neural Networks (RNNs); Emergent architectures; Deep learning frameworks; Summary; Chapter 2: A First Look at TensorFlow; A general overview of TensorFlow; What's new in TensorFlow v1.6?; Nvidia GPU support optimized; Introducing TensorFlow Lite; Eager execution; Optimized Accelerated Linear Algebra (XLA); Installing and configuring TensorFlow; TensorFlow computational graph; TensorFlow code structure; Eager execution with TensorFlow; Data model in TensorFlow; Tensor; Rank and shape; Data type; Variables; Fetches.
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|a Feeds and placeholdersVisualizing computations through TensorBoard; How does TensorBoard work?; Linear regression and beyond; Linear regression revisited for a real dataset; Summary; Chapter 3: Feed-Forward Neural Networks with TensorFlow; Feed-forward neural networks (FFNNs); Feed-forward and backpropagation; Weights and biases; Activation functions; Using sigmoid; Using tanh; Using ReLU; Using softmax; Implementing a feed-forward neural network; Exploring the MNIST dataset; Softmax classifier; Implementing a multilayer perceptron (MLP); Training an MLP; Using MLPs; Dataset description.
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| 505 |
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|a PreprocessingA TensorFlow implementation of MLP for client-subscription assessment; Deep Belief Networks (DBNs); Restricted Boltzmann Machines (RBMs); Construction of a simple DBN; Unsupervised pre-training; Supervised fine-tuning; Implementing a DBN with TensorFlow for client-subscription assessment; Tuning hyperparameters and advanced FFNNs; Tuning FFNN hyperparameters; Number of hidden layers; Number of neurons per hidden layer; Weight and biases initialization; Selecting the most suitable optimizer; GridSearch and randomized search for hyperparameters tuning; Regularization.
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| 505 |
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|a Dropout optimizationSummary; Chapter 4: Convolutional Neural Networks; Main concepts of CNNs; CNNs in action; LeNet5; Implementing a LeNet-5 step by step; AlexNet; Transfer learning; Pretrained AlexNet; Dataset preparation; Fine-tuning implementation; VGG; Artistic style learning with VGG-19; Input images; Content extractor and loss; Style extractor and loss; Merger and total loss; Training; Inception-v3; Exploring Inception with TensorFlow; Emotion recognition with CNNs; Testing the model on your own image; Source code; Summary; Chapter 5: Optimizing TensorFlow Autoencoders.
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| 500 |
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|a How does an autoencoder work?
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| 520 |
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|a Compliant with TensorFlow 1.7, this book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples of real-world data collection.
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| 650 |
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0 |
|a Machine learning.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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|a Python (Computer program language)
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| 650 |
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7 |
|a artificial intelligence.
|2 aat
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| 650 |
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7 |
|a Web programming.
|2 bicssc
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| 650 |
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7 |
|a Programming & scripting languages: general.
|2 bicssc
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| 650 |
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7 |
|a COMPUTERS
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Artificial intelligence
|2 fast
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| 650 |
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7 |
|a Machine learning
|2 fast
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| 650 |
|
7 |
|a Python (Computer program language)
|2 fast
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| 700 |
1 |
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|a Karim, Md. Rezaul.
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| 758 |
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|i has work:
|a Deep learning with TensorFlow (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGBwq3xDhmKVPPgpJgH3PP
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Zaccone, Giancarlo.
|t Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition.
|d Birmingham : Packt Publishing, ©2018
|
| 856 |
4 |
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|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5340529
|y Click for online access
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