Beginning Deep Learning with TensorFlow
Apress (Verlag)
978-1-4842-7914-4 (ISBN)
You’ll start with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs andRNNs.
Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer!
What You'll Learn
Develop using deep learning algorithms
Build deep learning models using TensorFlow 2
Create classification systems and other, practical deep learning applications
Who This Book Is For
Students, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.
Liangqu Long is a well-known deep learning educator and engineer in China. He is a successfully published author in the topic area with years of experience in teaching machine learning concepts. His two online video tutorial courses “Deep Learning with PyTorch” and “Deep Learning with TensorFlow 2” have received massive positive comments and allowed him to refine his deep learning teaching methods. Xiangming Zeng is an experienced data scientist and machine learning practitioner. He has over ten years of experience using machine learning and deep learning models to solve real world problems in both academia and professionally. Xiangming is familiar with deep learning fundamentals and mainstream machine learning libraries such as Tensorflow and scikit-learn.
Chapter 1: Introduction to Artificial Intelligence.- Chapter 2. Regression.- Chapter 3. Classification.- Chapter 4. Basic Tensorflow.- Chapter 5. Advanced Tensorflow.- Chapter 6. Neural Network.- Chapter 7. Backward Propagation Algorithm.- Chapter 8. Keras Advanced API.- Chapter 9. Overfitting.- Chapter 10. Convolutional Neural Networks.- Chapter 11. Recurrent Neural Network.- Chapter 12. Autoencoder.- Chapter 13. Generative Adversarial Network (GAN).- Chapter 14. Reinforcement Learning.- Chapter 15. Custom Dataset.
| Erscheinungsdatum | 04.02.2022 |
|---|---|
| Zusatzinfo | 323 Illustrations, black and white; XXIII, 713 p. 323 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | convolutional neural network • Deep learning • Deep Learning Fundamentals • Deep Learning real examples • Keras • machine learning • Neural networks • Recurrent Neural Network • tensorflow |
| ISBN-10 | 1-4842-7914-X / 148427914X |
| ISBN-13 | 978-1-4842-7914-4 / 9781484279144 |
| Zustand | Neuware |
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