AI Development (DAT301m)
Vietnamese title: Phát triển UDTTNT với TensorFlow
Credits: 3
Prerequisites: CPV301, DPL302m

Description:
Learn how to build real-world AI applications with TensorFlow! In this hands-on course, you’ll train neural networks for computer vision, apply techniques like data augmentation, dropout, and transfer learning, and build natural language processing systems using RNNs, GRUs, and LSTMs. Through project-based learning, you’ll gain practical experience creating image classifiers, object detectors, and text generators—preparing you with the skills to design and deploy powerful AI solutions.

Learning outcomes:
Understand the basics of TensorFlow and application of TensorFlow framework in Machine Learning, Deep Learning.
Implement a AI project includes steps: data collection, data wrangling, exploratory data analysis, model development, model evaluation, and reporting
Understand how to build a basic neural network in TensorFlow and train a neural network for a computer vision application.
Understan the convolutional Neural Networks in TensorFlow, include the strategies to prevent overfitting, including augmentation and dropout
Undesrstand the transfer learning and how learned features can be extracted from models
Understand basic concepts of sentiment in text, word Embeddings, sequence model and literature
Implementing natural language processing systems using TensorFlow
Understand the sequences, time series and prediction problems in TensorFlow
Understand how to apply RNNs, GRUs, and LSTMs as you train them using text repositories
Understand how to apply AI tools to complete a mini project

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises, assignments and labs given by instructor in class or at home and submit on time
- Use laptop in class only for learning purpose
- Promptly access to the FU FLM at https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
Jupyter Notebooks, Google Colab, ChatGPT, Github Copilot

Materials:
description: Convolutional Neural Networks in TensorFlow
author: Coursera
publisher: https://www.coursera.org/learn/convolutional-neural-networks-tensorflow
description: Guided Project
Object Localization with TensorFlow
author: Coursera
publisher: https://www.coursera.org/programs/fptu-fall-2024-zb2kn/projects/object-localization-tensorflow?source=search
description: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
author: Coursera
publisher: https://www.coursera.org/learn/introduction-tensorflow
description: Natural Language Processing in TensorFlow
author: Coursera
publisher: https://www.coursera.org/learn/natural-language-processing-tensorflow
description: Sequences, Time Series and Prediction
author: Coursera
publisher: https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction

Assessments:
category: Progress test
type: on-going
weight: 10.0
duration: 45'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, CLO6
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 30
grading_guide: - cover chapter 1,2,3
note: in class, by instructor
category: Report 1: Project Proposal [ Oral group presentation]
type: on-going
weight: 10.0
duration: 225'
question_type: CLO9, CLO10
no_question: Presentation, class
knowledge_and_skill: 1
grading_guide: - cover chapter 1
note: in class, by instructor
category: Report 2: Data Tasks [ Oral group presentation]
type: on-going
weight: 10.0
duration: 225'
question_type: CLO9, CLO10
no_question: Presentation, class
knowledge_and_skill: 1
grading_guide: - cover chapter 2
note: in class, by instructor
category: Report 3: Model & Results [ Oral group presentation]
type: on-going
weight: 30.0
duration: 225'
question_type: CLO9, CLO10
no_question: Presentation, class
knowledge_and_skill: 1
grading_guide: - cover chapter 3
note: in class, by instructor
category: Report 4: Final report & Group presentation
type: on-going
weight: 10.0
duration: 225'
question_type: CLO9, CLO10
no_question: Presentation, class
knowledge_and_skill: 1
grading_guide: - cover chapter 4
note: in class, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4, CLO5, CLO6, CLO7, CLO8
no_question: Multiple choices
Marked by Computer
knowledge_and_skill: 50
grading_guide: - cover chapter 1, 2, 3, 4
note: by exam board,
using computer

Schedule:
session: Course Introduction
learning_outcomes: T
materials: mat1
session: 1.1. Working with TensorFlow
learning_outcomes: T
session: 1.2. Machine learning and computer vision in TensorFlow
learning_outcomes: T
session: 1.3 Coding with TensorFlow in Google Colaboratory
learning_outcomes: T
session: 1.4 Computer Vision Neural Network
learning_outcomes: T
session: 1.5 Understand Convolutional Neural Networks in TensorFlow
learning_outcomes: T
session: 1.6 The techniques of Convolutional Neural Networks in TensorFlow
learning_outcomes: T
session: Discussion on the Capstone Project (CP) topic
learning_outcomes: U
session: Formation of teams/groups
learning_outcomes: U
session: Discuss and choose a project topic
learning_outcomes: U
session: Report 1: Project Proposal (Discussion)
learning_outcomes: U
session: 2.1 The large dataset in the Neural network
learning_outcomes: T
session: 2.2 Image Augmentation
learning_outcomes: T
session: 2.3 Overfitting and solutions
learning_outcomes: T
session: 2.4 The concept of transfer learning
learning_outcomes: T
session: 2.5 Create model with transferred features
learning_outcomes: T
session: 2.6 Exploring dropouts and Transfer Learning with Inception
learning_outcomes: T
session: 2.7 The multi-class classification: discusses and examples
learning_outcomes: T
session: Team Meeting: Project Proposal Writing (Report 1)
learning_outcomes: U
session: Team Meeting: Project Proposal Writing (Report 1)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Project Proposal (Report 1)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Project Proposal (Report 1)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Project Proposal (Report 1)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Project Proposal (Report 1)
learning_outcomes: U
session: Team Meeting: Data Collection(Present the issues discussed and agreed upon by the group) (Report 2)
learning_outcomes: U
session: 3.1 The word based encodings in TensorFlow
learning_outcomes: T
session: 3.2 Text to sequence in TensorFlow
learning_outcomes: T
session: 3.3 The Tokenizer in TensorFlow
learning_outcomes: T
session: 3.4 The padding in TensorFlow
learning_outcomes: T
session: 3.5 The vector in TensorFlow
learning_outcomes: T
session: 3.6 Classifier for the sarcasm dataset
learning_outcomes: T
session: 3.7 Subwords text encoder
learning_outcomes: T
session: 3.8 The Long Short Term Memory (LSTM) for NLP
learning_outcomes: T
session: 3.9 Exploring different sequence models
learning_outcomes: T
session: 3.10 Sequence models and literature for NLP
learning_outcomes: T
session: Team Meeting: Data Collection(Present the issues discussed and agreed upon by the group) (Report 2)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Data Tasks all phase (Report 2)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Data Tasks all phase (Report 2)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Data Tasks all phase (Report 2)
learning_outcomes: U
session: Progess test
learning_outcomes: T
session: 4.1 The introduction of Time series and its application
learning_outcomes: T
session: 4.2 Machine learning applied to time series
learning_outcomes: T
session: 4.3 Time series: Train, validation and test sets
learning_outcomes: T
session: 4.4 Time series: Metrics for evaluating performance
learning_outcomes: T
session: 4.5 Time series:tips to build the forecasting
learning_outcomes: T
session: 4.6 Deep neural networks for time series: Preparing features and labels
learning_outcomes: T
session: 4.7 Deep neural networks for time series: build the layer neural network
learning_outcomes: T
session: 4.8 Deep neural networks for time series: training, tuning and prediction
learning_outcomes: T
session: 4.9 Conceptual of Recurrent neural network for time series
learning_outcomes: T
session: 4.10 Recurrent neural network for time series: input, lambda layer, learnig rate, output
learning_outcomes: T
session: Team Meeting: Model & Results (Present the issues discussed and agreed upon by the group) (Report 3)
learning_outcomes: U
session: Team Meeting: Model & Results (Present the issues discussed and agreed upon by the group) (Report 3)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Model & Results (Report 3)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Model & Results (Report 3)
learning_outcomes: U
session: Team Meeting: Instructor's feedbacks for Model & Results (Report 3)
learning_outcomes: U
session: Team Meeting: Practice for Presentation of Capstone Project (Report 4)
learning_outcomes: U
session: Team Meeting: Practice for Presentation of Capstone Project (Report 4)
learning_outcomes: U
session: Team Meeting: Practice for Presentation of Capstone Project (Report 4)
learning_outcomes: U
session: Team Meeting: Practice for Presentation of Capstone Project (Report 4)
learning_outcomes: U
session: Review course
Results
learning_outcomes: U