| Description |
1 online resource (483 pages) |
| Contents |
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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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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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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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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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. |
| Note |
How does an autoencoder work? |
| Summary |
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. |
| Subject |
Machine learning.
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Artificial intelligence.
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Python (Computer program language)
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Artificial Intelligence |
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Machine Learning |
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Apprentissage automatique. |
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Intelligence artificielle. |
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Python (Langage de programmation) |
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artificial intelligence. |
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Web programming. |
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Programming & scripting languages: general. |
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Artificial intelligence |
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Machine learning |
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Python (Computer program language) |
| Added Author |
Karim, Md. Rezaul.
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| Other Form: |
Print version: Zaccone, Giancarlo. Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. Birmingham : Packt Publishing, ©2018 |
| ISBN |
9781788831833 (electronic bk.) |
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1788831837 (electronic bk.) |
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1788831101 |
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9781788831109 |
| Standard No. |
9781788831109 |
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