Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks

Applied Deep Learning A Case-Based Approach to Understanding Deep Neural Networks

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single ne...

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Detalles Bibliográficos
Autor principal: Michelucci, Umberto. author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berkeley, CA : Apress 2018.
Edición:1st ed. 2018.
Materias:
Artificial intelligence.
Python (Computer program language).
Open source software.
Computer programming.
Big data.
Artificial Intelligence.
Python.
Open Source.
Big Data.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630734806719
Tabla de Contenidos:
  • Chapter 1: Introduction
  • Chapter 2: Single Neurons
  • Chapter 3: Fully connected Neural Network with more neurons
  • Chapter 4: Neural networks error analysis
  • Chapter 5: Dropout technique
  • Chapter 6: Hyper parameters tuning
  • Chapter 7: Tensorflow and optimizers (Gradient descent, Adam, momentum, etc.)
  • Chapter 8: Convolutional Networks and image recognition
  • Chapter 9: Recurrent Neural Networks
  • Chapter 10: A practical COMPLETE example from scratch (put everything together)
  • Chapter 11: Logistic regression implement from scratch in Python without libraries. .

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