Computational Methods for Deep Learning Theory, Algorithms, and Implementations /
The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this...
I tiakina i:
| Kaituhi matua: | |
|---|---|
| Kaituhi rangatōpū: | |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
|
| Putanga: | 2nd ed. 2023. |
| Rangatū: | Texts in Computer Science,
|
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1007/978-981-99-4823-9 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
Rārangi ihirangi:
- 1. Introduction
- 2. Deep Learning Platforms
- 3. CNN and RNN
- 4. Autoencoder and GAN
- 5. Reinforcement Learning
- 6. CapsNet and Manifold Learning
- 7. Boltzmann Machines
- 8. Transfer Learning and Ensemble Learning.