Learning Deep Learning
Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this text can be used for students with prior programming experince but with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.
Table of Content:
1. The Rosenblatt Perceptron
2. Gradient-Based Learning
3. Sigmoid Neurons and Backpropagation
4. Fully Connected Networks Applied to Multiclass Classification
5. Fully Connected Networks Applied to Regression
6. Toward DL: Frameworks and Network Tweaks
7. Convolutional Neural Networks Applied to Image Classification
8. Deeper CNNs and Pretrained Models
9. Predicting Time Sequences with Recurrent Neural Networks
10. Neural Language Models and Word Embeddings
11. Sequence-to-Sequence Networks and Natural Language Translation
12. One-to-Many Network for Image Captioning
13. Attention and the Transformer
14. Medley of Additional Topics
15. Summary and Next Steps ONLINE CHAPTERS
16. Long Short-Term Memory
17. Text Autocompletion with LSTM and Beam Search
18. Word Embeddings from word2vec and GloVe"
| Book | |
|---|---|
| Author | Ekman |
| Pages | 760 |
| Year | 2022 |
| ISBN | 9789356063976 |
| Publisher | Pearson |
| Language | English |
| Uncategorized | |
| Subject | Computer Science / Artificial Intelligence (AI) |
| Edition | 1/e |
| Weight | 100 g |
| Dimensions | 24.4 x 20.3 x 3.7 cm |
| Binding | Paperback |