I had accomplished Andrew Ng’s DeepLearning.ai series in Coursera. I hope this review would be insightful for those whom might want to enter this field or simply consolidate your deep learning knowledge. This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too. DeepLearning.ai contains five courses which can be taken on Coursera. The five courses titles are:
- Neural Networks and Deep Learning.
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization.
- Structuring Machine Learning Projects.
- Convolutional Neural Networks.
- Sequence Models.
This is by far the best course series on deep learning that I've taken. Enjoy!
At last I've successfully completed the specialization and earned my certificate!
In course 1, it taught what is Neural Network, Forward & Backward Propagation and guide you to build a shallow network then stack it to be a deep network. Also, you will learn about the mathematics (Logistics Regression, Gradient Descent and etc.) related to it step by step. Andrew explained the maths in a very simple way that you would understand it without prior knowledge in linear algebra nor calculus. Last but not least, it discussed the activation functions and their Pros & Cons respectively.
In course 2, we would proceed to learn different regularization techniques and why they could reduce overfitting. On top of it, Andrew would teach you how to evaluate your deep learning model and note down his hyperparameter tuning technique in different situations (It is a gold for DL Enthusiast).
In course 3, Andrew would talk about how to set up evaluation metric since the highest accuracy model might not be the best in daily practice. He also discusses how to select and split the train/test/validation set and the methods we could use while lacking of data.
In course 4, Andrew would teach what is computer vision and how does a Convolutional Neural Network work by explaining the theory and maths of convolutional filter, Maxpooling filter, etc. On top of it, he would go through the structure of the famous pre-trained network: LeNet5, AlexNet’ VGG-16, and ResNet, then guide you to utilize them with transfer learning.
In the last course, it first talks about RNNs and LSTM then dive in to NLP and teach the major concepts such as Word Embedding, Word2Vec, Bleu Score, Beam Search, Skip Gram, etc. Lastly, you could practice how to build a machine translation model.
1- Beautifully drawn notes by Tess Ferrandez: