# Trends in Machine Learning for Unmanned Aerial Vehicle Applications - 鄭聖文
{%hackmd @mopcon/rkdTi5NTR %}
> 從這開始

* [投影片連結](https://drive.google.com/file/d/1UmSeU8Y63s4cASBFNWM_zi5ll2J9EIJx/view?usp=sharing)

* Introduction to Artificial Neural Network
* Neuron (perceptron)
* book: [hands-on convolutional neural with tensorflow](https://www.tenlong.com.tw/products/9781789130331)(
* (Deep) Neural network (multi-layer perceptron)
> 是一種通用函式擬合器，可以用來模擬任何物理現象的模型。
* book: [data-driven science and engineering](https://www.tenlong.com.tw/products/9781009098489?list_name=srh)
* 可以較輕鬆將類神經網路用線性代數表達。
* Softmax function
* Training a Neural Network (Back Propagation)
* How to Train an Artifical Neural Network(Prof. Youn-Long Lin)
* Residual Network (ResNet) 2015-
* Dropout
* Encoder-Decoder/AutoEncoder
> 萃取後，再重建。
* Object Detection and Segmentation
* Object tracking applications of drones
* Ukraine War
* Agriculture
* Firefighting drones in China
* Search and Rescue
* Border Patrol
* Bridge Inspection
* Feature extraction of convolutional neural network (CNN)
* low level feature: edges, dark, spots
* mid level feature: eyes, ears, nose
* high level feature: facial structure
* Max pooling
* [Pooling in Convolutional Neural Networks](https://www.digitalocean.com/community/tutorials/pooling-in-convolutional-neural-networks)
* Example: VGG16 network for image classification
* VGG16 (Visual Geometry Group Architecture 16), University of Oxford 2014
* Object Detection (YOLO v1)
* YOLO, You Only Look Once
* Fast R-CNN VGG-16 vs CNN
* mAP(mean Average Precision): 73.2% vs 63.4%
* FPS: 7 vs 45
* Semantic segmentation: segNet
* Auto-Driven vehicle
* Semantic segmentation: U-Net
* Learn to control: Reinforcement Learning
* Reinforcement Learning (RL)
* There is an Agent that interacts with the environment
* value function $V$ and quality function $Q$
* Deep Reinforcement Learning
* 傳統而言，以力學與數學進行分析與控制。
* With Deep Reinforcement Learning
* [Control of a Quadrotor with Reinforcement Learning, 2017](https://arxiv.org/pdf/1707.05110)
* Champion-level flight of Quadrotor with Reinforcement Learning
* Collision-free Flight with Reinforcement Learning
* Digial Twin
* Software simulators for robots
* Transformer Model
* transformer and attention model
* Encoder-Decoder architecture
* Embeddings are vectors in high dimensions
* variants
* Encoder-only
* Decoder-only
* Encoder-Decoder
* $Attention(Q, K, V)$
* Self-attention vs Cross-attention
* Vision Transformer
* Model generalization
* Visual Transformer (ViT)