Imagen de Portada

Deep Learning with Keras

  • Autor: Gulli, Antonio; Pal, Sujit
  • Editorial: Packt Publishing


Pendiente de reposición  

Precio: Sin confirmar

Si desea recibir información cuando este material se reponga, introduzca su correo..
Correo electronico: Política de Privacidad

Material válido paraClase de materialTipo de materialCarreraCurso


This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks. What you will learn •Optimize step-by-step functions on a large neural network using the Backpropagation algorithm •Fine-tune a neural network to improve the quality of results •Use deep learning for image and audio processing •Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases •Identify problems for which Recurrent Neural Network (RNN) solutions are suitable •Explore the process required to implement Autoencoders •Evolve a deep neural network using reinforcement learning


  • Nº de edición:
  • Año de edición: 2017
  • Número de reimpresión:
  • Año de reimpresión: 0
  • Dimensiones:
  • Páginas: 318
  • Soporte:
  • ISBN: 9781787128422

Icono de alerta Las cookies nos permiten ofrecer nuestros servicios. Al navegar por, consideramos que acepta el uso que hacemos de ellas.
Puede cambiar la configuración de cookies en cualquier momento. Para más información, puede consultar nuestro documento de politica de cookies