Machine learning
1. Why we are interested in machine learning; 2. Machine learning, statistics and data analytics; 3. Pattern recognition; 4. Neural networks and deep learning; 5. Learning clusters and recommendations; 6. Learning to take actions; 7. Where do we go from here?.
- Vị trí lưu trữ: 03 Quang Trung
- Tổng sách: 2
- Đang rỗi: 2
The book explores case studies of countries that have successfully managed to stem corruption, and the roles that the judicial system, auditors, ombudspersons, and the media can play in halting corruption. However, Rotberg argues, the essential ingredient to fighting corruption is a leader with the ...
- Vị trí lưu trữ: 03 Quang Trung
- Tổng sách: 2
- Đang rỗi: 2
Presenting to Introduction to deep learning. Conceptual foundations. Neural networks: the building blocks of deep learning. A brief history of deep learning. Convolutional and recurrent networks. Learning functions. The future of deep learning.
- Vị trí lưu trữ: 03 Quang Trung
- Tổng sách: 3
- Đang rỗi: 3
Presenting to What is data science?. What is data and what is a dataset?. The data science ecosystem. Machine learning 101. Standard data science tasks. Privacy and ethics. Future trends and principles of success.
- Vị trí lưu trữ: 03 Quang Trung
- Tổng sách: 3
- Đang rỗi: 3
This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine ...
- Phiên bản: 2nd ed.
- Vị trí lưu trữ: 03 Quang Trung
- Tổng sách: 2
- Đang rỗi: 2