Machine Learning with TensorFlow

Par : Nishant Shukla
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  • Nombre de pages251
  • PrésentationBroché
  • FormatGrand Format
  • Poids0.468 kg
  • Dimensions18,7 cm × 23,3 cm × 2,0 cm
  • ISBN978-1-61729-387-0
  • EAN9781617293870
  • Date de parution01/03/2018
  • ÉditeurManning
  • ContributeurKenneth Fricklas

Résumé

TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms.
Then, you'll move on to the money chapters : exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. What's Inside : Matching your tasks to the right machine-learning and deep-learning approaches ; Visualizing algorithms with TensorBoard ; Understanding and using neural networks.
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms.
Then, you'll move on to the money chapters : exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. What's Inside : Matching your tasks to the right machine-learning and deep-learning approaches ; Visualizing algorithms with TensorBoard ; Understanding and using neural networks.