Deep learning for coders with fastai and PyTorch : AI applications without a PhD

Deep learning for coders with fastai and PyTorch AI applications without a PhD

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fast...

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Bibliographic Details
Other Authors: Howard, Jeremy, author (author), Gugger, Sylvain, author
Format: eBook
Language:Inglés
Published: Sebastopol, California : O'Reilly Media, Inc [2020]
Edition:1st edition
Subjects:
Data mining.
Natural language processing (Computer science)
Machine learning.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631372206719
Table of Contents:
  • Part 1. Deep Learning Journey. Your Deep Learning Journey
  • From Model to Production
  • Data Ethics
  • Part 2. Understanding fastai's Applications. Under the Hood: Training a Digit Classifier
  • Image Classification
  • Other Computer Vision Problems
  • Training a State-of-the-Art Model
  • Collaborative Filtering Deep Dive
  • Tabular Modeling Deep Dive
  • NLP Deep Dive: RNNs
  • Data Munging with fastai's Mid-Level API
  • Part 3. Foundations of Deep Learning. A Language Model from Scratch
  • Convolutional Neural Networks
  • ResNets
  • Application Architectures Deep Dive
  • The Training Process
  • Part 4. Deep Learning from Scratch. A Neural Net from the Foundations
  • CNN Interpretation with CAM
  • A fastai Learner from Scratch
  • Concluding Thoughts.

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