"I hear and I forget, I see and I remember, I do and I understand."

- Confucius

Most machine learning books are very theoretical. Very few books explain how to use machine learning in a practical manner. In this series, I will share my learning experience of machine learning and deep learning, starting from 0.

I use Java daily, so in earlier times I tried to use Java for machine learning. You can see some posts use Java. Later, I found that Python is much more efficient for machine learning (coding-wise) so I switched to Python.

**Deep Learning**

- Set up development environment for deep learning
- Hardware setup for deep learning
- Popular deep learning libraries
- Neural Network Back-propagation
- Recurrent neural network back-propagation through time (BPTT)
- RNN Example - AI Programmer (1)
- RNN Example - AI Programmer (2)
- RNN Example - AI Programmer (3)
- RNN Network Structures
- Deep learning resources

**Machine Learning**

- What is machine learning?
- Decision Tree (Java)
- k-Nearest Neighbors (Java)
- K-means Clustering (Java)
- Logistic Regression
- Support Vector Machine (SVM)
- Logistic Regression vs. SVM
- How to handle noise data?
- How to handle over-fitting?

**Key Terms**

- Gradient descent optimization
- Activation functions
- Cost functions
- Dropout