Course description

This course is aimed at learners who already learned the math and code of Machine Learning (ML)/Deep Learning (DL) models, and wish to practice the full stack: shipping a complete ML/DL project, from design to deployment. Learners will get first-hand experience with best R&D and industrial practices of all components of a deep learning project.

Key points that make this course special:

1) The course is advised, built, mentored, and directly taught by deep learning experts and leaders at companies and R&D centers, including COTAI, AINovation, Cinnamon, TMA Solutions, HCMUT, HCMUS, JVN, and more.

2) Learners will be introduced to a unified framework for understanding ML/DL projects and their life-cycle. Also the associated challenges and best practices, or “tricks of the trade”, for planning, setting up, and successfully delivering ML/DL projects.

3) Learners will practice developing and deploying state-of-the-art integrated AI systems with language/visual understanding and reasoning capabilities. Specifically, learners will practice using the most important building blocks of deep learning: CNNs; RNNs & sequence models; embeddings; GANs, VAEs & generative models; deep RL; attention & memory mechanisms; neural-symbolic integration with reasoning, planning, and search mechanisms.

4) Learners will understand state of the art in their domains of interest: what’s possible, what to try next, and what still needs more R&D. We will also introduce most promising research areas to help learners keep up with fast-moving research areas such as transfer, few-shot, meta learning, and disentangled representation learning.

Target Audiances

  • Students
  • Working Professionals
  • Science/Technology Diploma or Graduates

Course Pre-requisites

Students should be familiar with/ be equipted with:

  • Programming (Python) (*)
  • Linear Algebra, Calculus and Optimization (*)
  • Logical and critical thinking (*)
  • AI Specialist course (by VTC Academy)

(*) Entrance test required

Course outline

 Session Content
1 Introduction to planning and project setup for ML projects
2 Choosing machine learning project: project impact and project cost estimation
3 Planning and project setup: metrics, baselines, and surveying
 Session Content
4 Technical overview: recent advances in Computer Vision
5 Technical overview: recent advances in Natural Language Processing
6 Technical overview: recent advances in other fields (e.g. recommender system, data mining, etc.)
Session Content
7 Lab instruction and guest lecture
8 Start course project: planning and project setup for course project
9 Data management for machine learning
10 Data-driven exploration and visualizations: tools and techniques
Session Content
11 Data integration: entity resolution and data fusion
12 Data wrangling
13 Data cleaning: error detection and repairing
Session Content
14 Lab instruction and guest lecture
15 Model selection and construction strategies
16 Training strategies: optimization and regularization for deep learning
Session Content
17 Training strategies: hyperparameter selection and experiment management
18 Debugging strategies
19 Lab instruction and guest lecture

 

Session Content
20 Deploying machine learning projects
21 Maintaining, monitoring, and improving machine learning project
22 Lab instruction and guest lecture
Session Content
23 Lab instruction and guest lecture
24 Lab instruction and guest lecture
25 Lab instruction and guest lecture
Session Content
26 Advanced data annotating and sampling: hard sampling, online learning, and active learning
27 Problems with large-scale machine learning projects
28 Lab instruction and guest lecture

 

Session Content
29 Reinforcement learning
30 Representation learning
31 Deep generative models
Session Content
32 Probabilistic graphical model and graph convolutional networks
33 Multimodal understanding and reasoning
34 Lab instruction and guest lecture
Session Content
35 Lab instruction and guest lecture
36 Lab instruction and guest lecture
37 End course project
38 Course review and closing


Course Execution

  •  108 Hours, 12 Weeks, 3 sessions/week, 3 hours/session
  •  Blended-Learning

Learning outcomes (Exit Competencies)

Upon completing this course, student should be able to:

Formulating the problem and estimating project cost. For example: how to prioritize projects & choosing goals; ii) how to choose/combine evaluation metrics; iii) how to choose appropriate baselines.

  • Finding (e.g., using web crawler, social listening), cleaning, labeling, and augmenting data (data wrangling).
  • Picking the right framework and compute infrastructure. Writing modular, hackable, and scalable code in major deep learning frameworks e.g. Tensorflow 2.0, PyTorch, Keras, etc.
  • Training models in parallel to find the right model architecture. Set up continuous integration system for your codebase, which will check functionality of code and evaluate the model about to be deployed. Troubleshooting training and ensuring reproducibility.
  • Deploying the model at scale. For example: package up the learned model as a REST API or deployable as a Docker container; deploy the learned model as a serverless function to Amazon Lambda.
  • Setting up mechanisms that alert AI developers when new data is sufficiently available for continual training, or when the incoming data distribution changes that needs to retrain the models.