📁 Created by: Muhammad Angga Muttaqien, in early 2018
🔬 Absolutely comfort lab for me to work around with my own AIs and to empirically observe how powerful and impactful these technologies are. The key research topics are listed as follows:
- Machine Learning
- Deep Learning (e.g. vision, sequence and reinforcement learning)
- Advanced Techniques (e.g. GAN, Zero-Shot Learning, Transformer, Multi-Agent Learning etc.)
Now, I'm conducting research on Reinforcement Learning
The best way to get deeper into AI technology is to get hands-on with it. In 2018-2020, I have an exciting plan to conduct a dozen experiments with numerous and diverse deep learning applications based on Computer Vision (CV), Natural Language Processing (NLP) and Reinforcement Learning (RL) technologies. Through learning-by-doing, the following is the list of applications I envision:
1. Object Classification
2. Object Detection
3. Real-time Object Detection
4. Semantic Segmentation
5. Instance Segmentation
6. Human Pose Detection
7. Visual Question Answering
1. Machine Translation System
2. Sentiment Analysis
3. Text Summarization
4. Topic Modeling
5. Chatbot
6. Image Captioning
7. Speech Recognition
1. Dynamic Programming Method for MDPs
2. Monte Carlo Method
3. Temporal-Difference Method (Sarsa, Sarsamax, Expected Sarsa)
4. Value-Based Method (DQN, Double-DQN, PER-DQN, Dueling-DQN, Noisy-DQN, Distributional-DQN, Rainbow-DQN)
5. Policy-Based Method (Reinforce, TRPO, PPO)
6. Actor-Critic Method (A2C/A3C, GAE, DDPG)
7. Multi-Agent Method (MADDPG, MFMARL)
The time will come soon.
You can instantly copy any folder on the project by executing this command:
svn checkout https://github.com/muhamuttaqien/AI-Lab/trunk/02-deep-learning
This lab requires Python 3.7.3 and the following Python libraries installed:
- Basic Libraries: NumPy, Matplotlib
- Domain-specific Libraries: OpenCV, NLTK, Gym
- Deep-learning Frameworks: Keras, PyTorch, TensorFlow, ReNom
📨 That's all, for any discussion kindly contact me here: muha.muttaqien@gmail.com