Time goes by quickly, and I also accomplished four courses of Deep learning specialization hosted by Deeplearning.ai. The teachear is still Andrew Ng. But at this time, I don’t feel sad anymore because Andrew is smiling at the end, “To do whatever you think is the best of what you can do for humanity.” Thanks to Deeplearning.ai, I master the basic concept of deep learning.

Outline

1. Neural networks and deep learning
2. Improving deep neural networks: hyperparameter tuning, regularization and optimization
3. Structuring machine learning projects
4. Convolutional neural networks
5. Sequence models

Program Assignments

anthonyweidai/AndrewNg-DeepLearning

Xiaohu Blogs

Summary for Deep Learning Hosted by Deeplearning.ai

How dose regularization work in ML and DL? - Class review

Weight Initialization Methods - Class review

Optimization algorithms for learning systems - Class review

Hyperparameters Tuning and Batch Normalization - Class review

Carrying out error analysis - Class review

Learning from Multiple Tasks - Class Review

Foundations of convolutional neural networks - Class review

Learning from Multiple Tasks - Class Review

Case study of classic networks - Class review

Inception network - Class Review

MoblieNets - Class review

Object detection - class review

YOLO algorithm

Semantic segmentation with U-Net - Class review

Face recognition - Class Review

Neural style transfer - Class review

Machine learning vs. deep learning

Machine learning and deep learning are both types of AI. In short, machine learning is AI that can automatically adpat with minimal human interference. Deep learning is a subset of machine learning that uses artificial neural networks to mimic the learning process of the human brain.

Reference

[1] Deeplearning.ai, Deep Learning Specialization
[2] Deep Learning vs. Machine Learning: Beginner’s Guide