Cover Image
Read Now

Math and architectures of deep learning

Discover what's going on inside the black box! To work with deep learning you'll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systemati...

Full description

Bibliographic Details
Main Authors: Chaudhury, Krishnendu, Ashok, Ananya H. (Author), Narumanchi, Sujay (Author), Shankar, Devashish (Author)
Format: eBook
Language:English
Published: Shelter Island, NY Manning Publications 2024
Subjects:
Apprentissage Profond / Mathématiques
Apprentissage Profond
Deep Learning (machine Learning) / Mathematics
Python (computer Program Language) / Fast
Deep Learning (machine Learning) / Fast
Python (computer Program Language) / Http://id.loc.gov/authorities/subjects/sh96008834
Deep Learning (machine Learning) / Http://id.loc.gov/authorities/subjects/sh2021006947
Python (langage De Programmation)
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • An overview of machine learning and deep learning
  • Vectors, matrices, and tensors in machine learning
  • Classifiers and vector calculus
  • Linear algebraic tools in machine learning
  • Probability distributions in machine learning
  • Bayesian tools for machine learning
  • Function approximation : how neural networks model the world
  • Training neural networks : forward propagation and backpropagation
  • Loss, optimization, and regularization
  • Convolutions in neural networks
  • Neural networks for image classification and object detection
  • Manifolds, homeomorphism, and neural networks
  • Fully Bayes model parameter estimation
  • Latent space and generative modeling, autoencoders, and variational autoencoders

Similar Items