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...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, California :
O'Reilly Media, Inc
[2020]
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631372206719 |
Tabla de Contenidos:
- 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.
