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Softmax Another popular classifier (like SVM) Generalized version of binary Logistic Regression classifier - Softmax function : - Loss function : cross-entropy function - Numerical Stability : exponential -> very large number -> normalize values - SVM vs Softmax Same score function Wx = b, different loss function. Softmax : Calculate probabilities for each classes. Easier to interpret. Softmax.p..

very deep networks - down-stream detection network - everything after a certain neuron state-of-the-art performance - 최신식, 최고의 성능 rectifying a image / image rectified image disparity ablation study - investigates the performance of an AI system by removing certain components to understand the contribution of the component to the overall system augmented - 증가시키다(무언가를 더해서) - make (something) great..

https://github.com/akshaybahadur21/QuickDraw GitHub - akshaybahadur21/QuickDraw: A simple implementation of Google's Quick, Draw Project for humans. 🖌️ 🖼️ A simple implementation of Google's Quick, Draw Project for humans. 🖌️ 🖼️ - GitHub - akshaybahadur21/QuickDraw: A simple implementation of Google's Quick, Draw Project for humans. 🖌️ 🖼️ github.com numpy matplotlib opencv-python keras pandas h5..

1. Determinant - Definition For $A=\begin{pmatrix}a_{11}a_{12}\\a_{21}a_{22}\end{pmatrix},A^{-1}=\frac{1}{a_{11}a_{12}-a_{21}a_{22}}\cdot\begin{pmatrix}a_{22}-a_{12}\\-a_{21}a_{22}\end{pmatrix}$ A is invertible if $\frac{1}{a_{11}a_{22}-a_{21}a_{12}}\neq 0$ $\therefore det(A) = \begin{vmatrix}a_{11}a_{12}\\a_{21}a_{22}\\\end{vmatrix} = \frac{1}{a_{11}a_{12}-a_{21}a_{22}}$ - for $3\times3$ matrix..

1. 데이터의 분석 - 인과관계에 따른 데이터 분석 (상관관계 x) - 데이터 전처리 : outlier 제거, 데이터 표준화 - 데이터 분석 : Error bar, 충분한 EDA(exploratory data analysis) - 데이터의 양 : Under-fitting / Over-fitting - training data != test data 2. Black box algorithm - deep learning 의 특성 : 왜 이런 결과가 나왔는지 설명하지 못하는 경우, 실제로 이를 적용하기 힘들 수 있음 - 성능 vs 설명력 - post-hoc explanability - one-pixel attack : 한 픽셀 차이로 결과가 변화할 수 있는 가능성. noise 문제 해결의 중요성 3. Web ..

Make a venv with the local python > 3.8 python -m venv torchaccel Activate the venv source torchaccel/bin/activate Get the installation code from here Run it on the (venv) shell. I used Pip. # MPS acceleration is available on MacOS 12.3+ pip3 install torch torchvision torchaudio To check if torch is installed, in python, run import torch x = torch.rand(5, 3) print(x) you may check it from the of..

cd opencv-2.4.13.6 mkdir build cd build cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX="path/to/yout/venv" .. make sudo make install //to check if opencv2 is installed import cv2 print(cv2.__version__)

일단 필요한 python 버전을 다운로드 받는다. 나의 경우 3.7 python3.7 -m venv Lab10 source Lab10/bin/activate pip install -r requirements.txt // install -r : install modules written in requirements.txt file