Deep Learning (DPL303m)
Vietnamese title: Học sâu
Credits: 3
Prerequisites: AIL304m

Description:
In this course, student will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

Learning outcomes:
Build CNN models for visual detection and recognition tasks and use neural style transfer to generate art from various types of data.
Could build and train recurrent neural networks (RNNs), work with NLP and word embeddings, use HuggingFace tokenizers, and transformer models to solve different NLP tasks.
Understand complex ML settings, diagnose errors in a machine learning system, prioritize strategies for reducing errors, and apply end-to-end learning, transfer learning, and multi-task learning.
Practical project with Tensorflow

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 https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
- Jupyter notebook

Materials:
description: https://www.coursera.org/specializations/deep-learning
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning
author: Coursera
publisher: Coursera
description: https://www.coursera.org/learn/nlp-sequence-models?specialization=deep-learning
author: Coursera
publisher: Coursera
description: https://www.coursera.org/projects/tensorflow-for-beginners-basic-binary-image-classification-v2
author: Coursera
publisher: Coursera
note: Project 1
description: https://www.coursera.org/projects/tensorflow-for-cnns-learn-and-practice-cnns
author: Coursera
publisher: Coursera
note: Project 2
description: https://www.coursera.org/projects/object-localization-tensorflow
author: Coursera
publisher: Coursera
note: Project 3
description: https://www.coursera.org/projects/tweet-emotion-tensorflow
author: Coursera
publisher: Coursera
note: Project 4

Assessments:
category: Lab
type: on-going
weight: 20.0
duration: 1 day
question_type: Lab1: CLO1;
LAB2: CLO2
grading_guide: PT1: cover content of Module 1;
PT2: cover content of Module 2
note: at home, by instructor
category: Progress test
type: on-going
weight: 10.0
duration: 20'-30'
question_type: PT1: CLO1;
PT2: CLO2
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 20
grading_guide: PT1: cover content of Module 1;
PT2: cover content of Module 2
note: in class, by instructor
category: Project
type: on-going
weight: 40.0
duration: 2 weeks
question_type: Project 1-3: CLO1,CLO4;
Project 4: CLO2,CLO4
grading_guide: Project 1-3: cover content of Module 1;
Project 4: cover content of Module 2
note: at home, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 60'
question_type: CLO1, CLO2, CLO3
no_question: Multiple choices
Marked by Computer
knowledge_and_skill: 50
grading_guide: all studied modules
note: by exam board,
using computer

Schedule:
session: Introduction CNN
1.1 Foundations of CNNs
learning_outcomes: T
materials: DPL303m
session: 1.1 Foundations of CNNs
learning_outcomes: T
session: 1.1 Foundations of CNNs
learning_outcomes: T
session: Guided Project 1: TensorFlow for AI: Applying Image Convolution
learning_outcomes: U
session: Guided Project 1: TensorFlow for AI: Applying Image Convolution
learning_outcomes: U
session: Guided Project 1: TensorFlow for AI: Applying Image Convolution
learning_outcomes: U
session: 1.2 Deep convolutional models: case studies
learning_outcomes: T
session: 1.2 Deep convolutional models: case studies
learning_outcomes: T
session: 1.2 Deep convolutional models: case studies
learning_outcomes: T
session: Review Project 1
learning_outcomes: U
session: Review Project 1
learning_outcomes: U
session: Guided Project 2: TensorFlow for CNNs: Learn and Practice CNNs
learning_outcomes: U
session: Guided Project 2: TensorFlow for CNNs: Learn and Practice CNNs
learning_outcomes: U
session: Guided Project 2: TensorFlow for CNNs: Learn and Practice CNNs
learning_outcomes: U
session: 1.3 Object detection
learning_outcomes: T
session: 1.3 Object detection
learning_outcomes: T
session: 1.3 Object detection
learning_outcomes: T
session: 1.3 Object detection
learning_outcomes: T
session: 1.3 Object detection
learning_outcomes: T
session: 1.3 Object detection
learning_outcomes: T
session: Lab 1 (Car detection)
learning_outcomes: U
session: Lab 1 (Car detection)
learning_outcomes: U
session: Lab 1 (Car detection)
learning_outcomes: U
session: Review project 2
learning_outcomes: U
session: Review project 2
learning_outcomes: U
session: 4.4 Special applications: Face recognition and Neural style transfer
learning_outcomes: T
session: 4.4 Special applications: Face recognition and Neural style transfer
learning_outcomes: T
session: 4.4 Special applications: Face recognition and Neural style transfer
learning_outcomes: T
session: 4.4 Special applications: Face recognition and Neural style transfer
learning_outcomes: T
session: Guided project 3: Using Tensorflow for Image Style Transfer
learning_outcomes: U
session: Guided project 3: Using Tensorflow for Image Style Transfer
learning_outcomes: U
session: Guided project 3: Using Tensorflow for Image Style Transfer
learning_outcomes: U
session: Review Project 3
learning_outcomes: U
session: Review Lab 1
learning_outcomes: U
session: Progress test 1
learning_outcomes: T
session: 2.1 Recurrent Neural Networks
learning_outcomes: T
session: 2.1 Recurrent Neural Networks
learning_outcomes: T
session: 2.1 Recurrent Neural Networks
learning_outcomes: T
session: 2.1 Recurrent Neural Networks
learning_outcomes: T
session: 2.1 Recurrent Neural Networks
learning_outcomes: T
session: 2.1 Recurrent Neural Networks
learning_outcomes: T
session: Guiding project 4: Tensorflow : Analyse de Sentiments avec Word Embedding
learning_outcomes: U
session: Guiding project 4 :Tensorflow : Analyse de Sentiments avec Word Embedding
learning_outcomes: U
session: Guiding project 4 :Tensorflow : Analyse de Sentiments avec Word Embedding
learning_outcomes: U
session: 5.2 Natural Language Processing and Word Embeddings
learning_outcomes: T
session: 5.2 Natural Language Processing and Word Embeddings
learning_outcomes: T
session: 5.2 Natural Language Processing and Word Embeddings
learning_outcomes: T
session: Lab 2 (Operations on Word Vectors - Debiasing)
learning_outcomes: U
session: Lab 2 (Operations on Word Vectors - Debiasing)
learning_outcomes: U
session: 5.3 Sequence models and Attention mechanism
learning_outcomes: T
session: 5.3 Sequence models and Attention mechanism
learning_outcomes: T
session: 5.3 Sequence models and Attention mechanism
learning_outcomes: T
session: 5.3 Sequence models and Attention mechanism
learning_outcomes: T
session: 5.4 Transformer Network
learning_outcomes: T
session: 5.4 Transformer Network
learning_outcomes: T
session: Review Lab 2
learning_outcomes: U
session: Progess test 2
learning_outcomes: T
session: Review Project 4
learning_outcomes: U
session: Review
learning_outcomes: U
session: Review
learning_outcomes: U