[major=SE | course_id=DPL303m | syllabus_id=12036 | semester=8 | combo=SE_COM7.1 | title=Deep Learning]
Course ID: DPL303m
Title EN: Deep Learning
Title VI: Học sâu
Credits: 3.0
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.
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
Course type: coursera_hybrid
Decision: 1286/QĐ-ĐHFPT dated 11/22/2024
Approved date: 2024-11-22
CLO1: Build CNN models for visual detection and recognition tasks and use neural style transfer to generate art from various types of data.
[1] 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; completion_criteria=; clos=>0
[2] category=Progress test; type=on-going; weight=10.0; duration=20'-30'; question_type=PT1: CLO1;
PT2: CLO2; grading_guide=PT1: cover content of Module 1;
PT2: cover content of Module 2; completion_criteria=; clos=>0
[3] 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; completion_criteria=; clos=>0
[4] category=Final exam; type=Final exam; weight=30.0; duration=60'; question_type=CLO1, CLO2, CLO3; grading_guide=all studied modules; completion_criteria=; clos=4
[1] desc=https://www.coursera.org/specializations/deep-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[2] desc=https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[3] desc=https://www.coursera.org/learn/nlp-sequence-models?specialization=deep-learning; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=
[4] desc=https://www.coursera.org/projects/tensorflow-for-beginners-basic-binary-image-classification-v2; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=Project 1
[5] desc=https://www.coursera.org/projects/tensorflow-for-cnns-learn-and-practice-cnns; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=Project 2
[6] desc=https://www.coursera.org/projects/object-localization-tensorflow; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=Project 3
[7] desc=https://www.coursera.org/projects/tweet-emotion-tensorflow; author=Coursera; publisher=Coursera; edition=; isbn=; main=True; online=True; note=Project 4
Session 1: Introduction CNN
1.1 Foundations of CNNs
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Foundations of CNNs
Materials: DPL303m
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Session 2: 1.1 Foundations of CNNs
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Foundations of CNNs
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Session 3: 1.1 Foundations of CNNs
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Foundations of CNNs
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Session 4: Guided Project 1: TensorFlow for AI: Applying Image Convolution
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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CLO2: 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.
Session 1: Guided Project 1: TensorFlow for AI: Applying Image Convolution
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 2: Guided Project 1: TensorFlow for AI: Applying Image Convolution
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 3: 1.2 Deep convolutional models: case studies
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Deep convolutional models: case studies
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Session 4: 1.2 Deep convolutional models: case studies
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Deep convolutional models: case studies
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CLO3: 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.
Session 1: 1.2 Deep convolutional models: case studies
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Deep convolutional models: case studies
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Session 2: Review Project 1
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 3: Review Project 1
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 4: Guided Project 2: TensorFlow for CNNs: Learn and Practice CNNs
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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CLO4: Practical project with Tensorflow
Session 1: Guided Project 2: TensorFlow for CNNs: Learn and Practice CNNs
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 2: Guided Project 2: TensorFlow for CNNs: Learn and Practice CNNs
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 3: 1.3 Object detection
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Object detection
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Session 4: 1.3 Object detection
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Object detection
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Session 1: 1.3 Object detection
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Object detection
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Session 2: 1.3 Object detection
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Object detection
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Session 3: 1.3 Object detection
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Object detection
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Session 4: 1.3 Object detection
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Object detection
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Session 1: Lab 1 (Car detection)
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Link in the Materials
Download: Do lab 1

Session 2: Lab 1 (Car detection)
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Link in the Materials
Download: Do lab 1

Session 3: Lab 1 (Car detection)
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Link in the Materials
Download: Do lab 1

Session 4: Review project 2
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 1: Review project 2
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 2: 4.4 Special applications: Face recognition and Neural style transfer
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Special applications: Face recognition and Neural style transfer
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Session 3: 4.4 Special applications: Face recognition and Neural style transfer
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Special applications: Face recognition and Neural style transfer
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Session 4: 4.4 Special applications: Face recognition and Neural style transfer
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Special applications: Face recognition and Neural style transfer
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Session 1: 4.4 Special applications: Face recognition and Neural style transfer
Topic: Offline; Teaching type: CLO1; LO: T
ITU: Slide, Videos of Special applications: Face recognition and Neural style transfer
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Session 2: Guided project 3: Using Tensorflow for Image Style Transfer
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 3: Guided project 3: Using Tensorflow for Image Style Transfer
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 4: Guided project 3: Using Tensorflow for Image Style Transfer
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 1: Review Project 3
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 2: Review Lab 1
Topic: Offline; Teaching type: CLO1; LO: U
ITU: Student's lab
Download: Presentation

Session 3: Progress test 1
Topic: Offline; Teaching type: CLO1; LO: T
ITU: - Content module 1.
Download: - Review module 1
'- Do progress test 2

Session 4: 2.1 Recurrent Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Recurrent Neural Networks
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Session 1: 2.1 Recurrent Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Recurrent Neural Networks
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Session 2: 2.1 Recurrent Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Recurrent Neural Networks
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Session 3: 2.1 Recurrent Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Recurrent Neural Networks
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Session 4: 2.1 Recurrent Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Recurrent Neural Networks
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Session 1: 2.1 Recurrent Neural Networks
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Recurrent Neural Networks
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Session 2: Guiding project 4: Tensorflow : Analyse de Sentiments avec Word Embedding
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 3: Guiding project 4 :Tensorflow : Analyse de Sentiments avec Word Embedding
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 4: Guiding project 4 :Tensorflow : Analyse de Sentiments avec Word Embedding
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 1: 5.2 Natural Language Processing and Word Embeddings
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Natural Language Processing and Word Embeddings
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Session 2: 5.2 Natural Language Processing and Word Embeddings
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Natural Language Processing and Word Embeddings
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Session 3: 5.2 Natural Language Processing and Word Embeddings
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Natural Language Processing and Word Embeddings
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Session 4: Lab 2 (Operations on Word Vectors - Debiasing)
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Lab 2 file
Download: Do Lab 2
Session 1: Lab 2 (Operations on Word Vectors - Debiasing)
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Lab 2 file
Download: Do Lab 2

Session 2: 5.3 Sequence models and Attention mechanism
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Sequence models and Attention mechanism
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Session 3: 5.3 Sequence models and Attention mechanism
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Sequence models and Attention mechanism
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Session 4: 5.3 Sequence models and Attention mechanism
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Sequence models and Attention mechanism
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Session 1: 5.3 Sequence models and Attention mechanism
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Sequence models and Attention mechanism
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Session 2: 5.4 Transformer Network
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Transformer Network
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Session 3: 5.4 Transformer Network
Topic: Offline; Teaching type: CLO2; LO: T
ITU: Slide, Videos of Transformer Network
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Session 4: Review Lab 2
Topic: Offline; Teaching type: CLO2; LO: U
ITU: Student's lab
Download: Presentation
Session 1: Progess test 2
Topic: Offline; Teaching type: CLO2; LO: T
ITU: - Content module 2.
Download: - Review module 2
'- Do progress test 2

Session 2: Review Project 4
Topic: Offline; Teaching type: CLO3,CLO4; LO: U
ITU: Link in the Materials
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Session 3: Review
Topic: Offline; Teaching type: All CLOs; LO: U
ITU: All Slides
Download: Review Slide

Session 4: Review
Topic: Offline; Teaching type: All CLOs; LO: U
ITU: All Slides
Download: Review Slide