Description
This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction of classical machine learning and review some key concepts required to understand deep learning.In the second part, we discuss how deep learning differs from classical machine learning and explain why it is effective in dealing with complex problems such as the image and natural language processing. Various CNN and RNN models will becovered. In the third part, we introduce the deep reinforcement learning and its applications.This course also gives coding labs. We will use Python 3 as the main programming language throughout the course. Some popular machine learning libraries such as Scikit-learn and Tensorflow will be used and explained in detials.
Syllabus
| Lecture 01 | Introduction/Scientific Python 101 |
| Lecture 02 | Linear Algebra/Data Exploration & PCA |
| Lecture 03 | Probability & Information Theory/Decision Trees & Random Forest |
| Lecture 04 | Numerical Optimization/Perceptron & Adaline/Regression |
| Lecture 05 | Learning Theory & Regularization /Regularization |
| Lecture 06 | Probabilistic Models/Logistic Regression & Metrics |
| Lecture 07 | Non-Parametric Methods & SVMs/SVMs & Scikit-Learn Pipelines |
| Lecture 08 | Cross Validation & Ensembling/CV & Ensembling |
| Competition01 | Predicting Appropriate Response |
| Lecture 09 | Large-Scale Machine Learning |
| Lecture 10 | Neural Networks: Design/TensorFlow101 & Word2Vec |
| Lecture 11 | Neural Networks: Optimization & Regularization |
| Lecture 12 | Convolutional Neural Networks/ |
| Nuts and Bolts of Convolutional Neural Networks/Visualization and Style Transfer | |
| Competition 02 | Image Object Detection & Localization |
| Lecture 13 | Recurrent Neural Networks/Seq2Seq Learning for Machine Translation |
| Competition 03 | Image Caption |
| Lecture 14 | Unsupervised Learning/Autoencoders/GANs |
| Competition 04 | Reverse Image Caption |
| Lecture 15 | Semisupervised/Transfer Learning and the Future |
| Lecture 16 | Reinforcement Learning/Q-learning |
| Lecture 17 | Deep Reinforcement Learning/ DQN & Policy Network |
| Competition 05 | You Draw I Draw |
Reference Books
.png)
