Ann Architecture Machine Learning Projects
Buch, Englisch, 726 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1124 g
ISBN: 978-1-4842-6149-1
Verlag: Apress
Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects.
After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures—starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis.
This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are.
What You'll Learn
Beginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies.
After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures—starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis.
This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are.
What You'll Learn
- Develop Machine Learning Applications
- Translate languages using neural networks
- Compose images with style transfer
Beginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies.
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Professional/practitioner
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Weitere Infos & Material
Chapter 1: TensorFlow Jump Start.- Chapter 2: A Closer Look at TensorFlow.- Chapter 3: Deep Dive in tf.keras.- Chapter 4: Transfer Learning.- Chapter 5: Neutral Networks for Regression.- Chapter 6: Estimators.- Chapter 7: Text Generation.- Chapter 8: Language Translation.- Chapter 9: Natural Langauge.- Chapter 10: Image Captioning.- Chapter 11: Time Series.- Chapter 12: Style Transfer.- Chapter 13: Image Generation- Chapter 14: Image Translation.
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