Python artificial intelligence projects for beginners get up and running with artificial intelligence using 8 smart and exciting AI applications
Build smart applications by implementing real-world artificial intelligence projects Key Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI proj...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham ; Mumbai :
Packt
2018.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630525006719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Table of Contents
- Preface
- Chapter 1: Building Your Own Prediction Models
- Classification overview and evaluation techniques
- Evaluation
- Decision trees
- Common APIs for scikit-learn classifiers
- Prediction involving decision trees and student performance data
- Summary
- Chapter 2: Prediction with Random Forests
- Random forests
- Usage of random forest
- Predicting bird species with random forests
- Making a confusion matrix for the data
- Summary
- Chapter 3: Applications for Comment Classification
- Text classification
- Machine learning techniques
- Bag of words
- Detecting YouTube comment spam
- Word2Vec models
- Doc2Vec
- Document vector
- Detecting positive or negative sentiments in user reviews
- Summary
- Chapter 4: Neural Networks
- Understanding neural networks
- Feed-forward neural networks
- Identifying the genre of a song with neural networks
- Revising the spam detector to use neural networks
- Summary
- Chapter 5: Deep Learning
- Deep learning methods
- Convolutions and pooling
- Identifying handwritten mathematical symbols with CNNs
- Revisiting the bird species identifier to use images
- Summary
- Other Books You May Enjoy
- Index.
