CMPE 258: Deep Learning Lecture Notes
Instructor Information
Spring 2025 Project Key Components
Option 1: Deep Learning Models
- Goal: Build a full pipeline of a Deep Learning application with model training.
- Key Steps:
- Question/Problem Formulation: Propose your own application and problem.
- Data Acquisition and Processing: Identify suitable datasets for training and evaluation.
- State-of-the-Art Models: Assess and compare state-of-the-art open-source models.
- Model Training and Evaluation: Modify model architecture, tune parameters, and evaluate.
- Inference and Optimization: Create an end-to-end application for real-time tests and optimize inference.
Option 2: LLMs and AI Agent
- Goal: Develop AI applications using LLMs (Large Language Models).
- Key Steps:
- Question/Problem Formulation: Propose your own AI application.
- Data Acquisition: Gather few-shot examples for evaluation.
- LLM Models Comparison: Compare state-of-the-art LLM models.
- Feature Implementation: Implement parameter-efficient fine-tuning or AI agent tools.
- AI App Development: Conduct comprehensive testing and UI evaluation.
Default Projects
- AI Object Detection for Smart City
- AI Agent for Airplane Pilot: Develop an advanced airplane pilot assistance system using LLM agents for tasks like radio command translation, manual assistance, and flight route planning.
Common Datasets for ML Projects
- Image Classification: ImageNet, CIFAR-10/100, MNIST, FashionMNIST
- Object Detection: COCO, Kitti, Waymo, Argo, nuScenes
- NLP: IMDB Reviews, SQuAD, WMT19
- Speech Recognition: LibriSpeech, Mozilla Common Voice
- Time Series Analysis: UCI Repository
- Video Analysis: Kinetics
- Medical Imaging: MURA, Chest X-Ray
Project Requirements
- Team Requirements: 1-3 members, additional documentation for more members.
- Milestones: Proposal, presentation, final report.
- Assessment: Based on problem importance, solution novelty, technical excellence, complexity, creativity, code clarity, presentation, and documentation.
- Innovation Encouragement: Avoid basic tutorials, focus on substantial modifications and open-source alternatives.
Computing Resources
- Option 1: Personal machine
- Option 2: Cloud resources (Google Colab, AWS, Azure)
- Option 3: SJSU CoE HPC (NVIDIA P100 GPU)
- Note: No guarantee of resource availability; students cover personal cloud costs.
Deep Learning Concepts
- Model Building: Similar to training a dog, involves learning from data rather than explicit programming.
AI History Highlights
- Birth of AI (1943-1956): Introduction of artificial neurons, Turing Test, high-level languages.
- AI Winters: Periods of reduced interest and funding.
- Current AI Spring: Advances in translation, image recognition, game-playing systems.
- Deep Learning Milestones: Introduction of "Deep Learning," success of AlexNet, rise of autonomous vehicles.
Influential Figures in Deep Learning
- Geoffrey Hinton: Pioneered neural networks, developed AlexNet.
- Yoshua Bengio and Yann LeCun: Major contributors to AI breakthroughs.
Important AI Contributions
- AlexNet's Success: Won ImageNet challenge, leading to Google acquiring DNNResearch.
- OpenAI and Influence: Founded by Ilya Sutskever, furthered AI research and development.
This lecture provides a comprehensive overview of the deep learning landscape, project guidelines for students, and historical context of AI development.