ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS :: APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE.
Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and KerasKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and seman...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Place of publication not identified]
PACKT Publishing,
2020.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and KerasKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook DescriptionAdvanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models - autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is forThis is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended. |
| Beschreibung: | 1 online resource |
| ISBN: | 183882572X 9781838825720 |
Internformat
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| 520 | |a Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and KerasKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook DescriptionAdvanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models - autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is forThis is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended. | ||
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| spelling | Atienza, Rowel. ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. [Place of publication not identified] PACKT Publishing, 2020. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (EBSCO, viewed May 28, 2020). Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and KerasKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook DescriptionAdvanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models - autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is forThis is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended. TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Réseaux neuronaux (Informatique) Python (Langage de programmation) artificial intelligence. aat COMPUTERS / Natural Language Processing. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2384229 Volltext |
| spellingShingle | Atienza, Rowel ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Réseaux neuronaux (Informatique) Python (Langage de programmation) artificial intelligence. aat COMPUTERS / Natural Language Processing. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/names/n2019020612 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D000069550 |
| title | ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. |
| title_auth | ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. |
| title_exact_search | ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. |
| title_full | ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. |
| title_fullStr | ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. |
| title_full_unstemmed | ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. |
| title_short | ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : |
| title_sort | advanced deep learning with tensorflow 2 and keras apply dl gans vaes deep rl unsupervised learning object detection and segmentation and more |
| title_sub | APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE. |
| topic | TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Réseaux neuronaux (Informatique) Python (Langage de programmation) artificial intelligence. aat COMPUTERS / Natural Language Processing. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| topic_facet | TensorFlow. Machine learning. Artificial intelligence. Neural networks (Computer science) Python (Computer program language) Artificial Intelligence Neural Networks, Computer Machine Learning Apprentissage automatique. Intelligence artificielle. Réseaux neuronaux (Informatique) Python (Langage de programmation) artificial intelligence. COMPUTERS / Natural Language Processing. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2384229 |
| work_keys_str_mv | AT atienzarowel advanceddeeplearningwithtensorflow2andkerasapplydlgansvaesdeeprlunsupervisedlearningobjectdetectionandsegmentationandmore |