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portada Beginning Deep Learning With Tensorflow: Work With Keras, Mnist Data Sets, and Advanced Neural Networks
Type
Physical Book
Author
Publisher
Year
2022
Language
English
Pages
736
Format
Paperback
Dimensions
23.4x15.6x3.8 cm
Weight
1.02 kg.
ISBN13
9781484279144
Edition No.
1
Categories

Beginning Deep Learning With Tensorflow: Work With Keras, Mnist Data Sets, and Advanced Neural Networks

Liangqu Long (Author) · Xiangming Zeng (Author) · Apress · Paperback

Beginning Deep Learning With Tensorflow: Work With Keras, Mnist Data Sets, and Advanced Neural Networks - Long, Liangqu ; Zeng, Xiangming

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Synopsis "Beginning Deep Learning With Tensorflow: Work With Keras, Mnist Data Sets, and Advanced Neural Networks "

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. 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 and RNNs. 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 LearnDevelop using deep learning algorithmsBuild deep learning models using TensorFlow 2Create classification systems and other, practical deep learning applicationsWho This Book Is ForStudents, 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.

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