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Intro
About ML
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General topics
Classification vs Regression
Complex Step Differentiation
feature processing
Tree Ensemble vs Random Forest
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Scikit
about scikit
fetch_openml
SVC
knn
cross validation
classification_report
Decision function
fscoreSupport
PlotConfusionMatrix
roc curve
preprocessing
pipeline
DecisionTreeClassifier
CalibratedClassifierCV
check_estimator
logistics regression
classification metrics
Speeding up scikit-learn
Classification
Binary Classification
linear classifiers
Linear Classifier
ReParametrization
Linear Classifier Random
Linear Classifier MOONs
Linear Classifier breast
SVM - support vector machine
Support Vector Machines (SVM)
SVM Rand classification
SVM breast classification
k nearest neighbors
KNN
curse of dimensionality
KNN random circles
KNN brest
Curse lab
training
mnist multicalass example
K FOLD
Data Split
Training Process Comparison
resampling
multi-class
precision and recall
Balanced Accuracy
Roc
cost
classification labs
Performance Scores demo
MNIST KNN
MNIST SVM
Decision Functions Demos
Performance Scores
Calibrating Imbalanced
Understanding the AUC
TensorBoard
Feature Engineering
Normalizing and Standardizing
Non Separable Problem
technics list
Features Transform case1
Features Transform case2
Outline
Kernel Tricks
Kernel SVM
mnist example with kernel trick
fashion-mnist
AutoML
Statistical Classification
logistic regression
Demo Lab Logistic Regression
Cross Entropy
Mnist Logistic Regression
MAP Classifier
Naive Bayes Classifier
Naive Bayes Classifier
Decision Trees
Decision Trees
Circles Decision Tree Classifier
Heart Disease Tree Classifier
Linear Regression
Linear Regression: Model Imperfection and Steps to Implement Regression
Polynomial regression
Phase Estimation
Polynomial Fit
AI Program
Ridge Regression
R2 Score
Least Squares with
\({L}^{1}\)
/
\({L}^{2}\)
Regularization
Polynomial Fit with LASSO Regularization
outliers
huber
LS outiner
RANSAC
Polynomial Fit with RANSAC
Weighted Least Squares
Non-Linear Least Squares (NLLS)
ElasticNet
Non parametric regression
kernel regression
Multivariate Kernel Regression
Kernel Regression
boston house Kernel Regression
Polynomial Regression
Polynomial Regression
Spline Interpolation
Regression Tree
Regression - Decision Tree
Ensemble Methods
Bias Variance Tradeoff
Bagging
Out-of-Bag Error (OOB)
Variance and Bias Trade-off
Random Forest
feature importances
MDI
permutation importance
Random Forests
Gradient boosting
Gradient Boosting
ada boost
Adaptive Boosting
LightGBM
XGBoost
Clustering
clustering
K-Means
K-Means
color space quantization
K-Means super pixcel
GMM
Gaussian Mixture Models (GMM)
Hierarchical clustering
Hierarchical Clustering
Density
DBSCAN Demo
HDBSCAN
HDBSCAN Demo
Dimensionality Reduction
pca
Rotation
Eigen Decomposition
Full PCA
oose
svd (pca)
Gene PCA DEMO LAB
PCA DEMO FACES
PCA Brest Cancer
non Linear PCA
Kernel PCA
Dual PCA
Dimensionality Reduction - Kernel PCA
MDS
MultiDimensional Scaling (MDS)
Manifold Learning
isoMap
Manifold Learning - IsoMap
Spectral Clustering
TSNE
t-SNE Demo
Entropy
Umap
parametric Embedding
umap demo
manifold concepts demo
Manifold Learning - multi model compare
Anomaly detection
zscore
Mahalanobis
Time Series
Local Outlier Factor
Local Outlier Factor
LOF Exercise
Isolation forest
Anomaly Detection - Isolation Forest
Isolation Forest - Exercise
text Analysis
text opts
AI Computer Vision
Labs
Convolution in Deep Learning
Fully Connected Drawbacks
CNN for Frequency Estimation Problem
Convolution for Frequency Estimation
2D Convolution
Layers in Convolutional Neural Networks
Pooling in Convolutional Neural Networks
Batch Normalization
demo batch norm
Common Filters for 2D Convolution
Common Filters for 2D Convolution
Receptive Field (RF)
Calc RF
Encode-Decode Networks
skip connections
cifar10-2d-convolution
Image Classification Fashion MNIST
studentTeacherDemo
pytorch for CV
pytorch layers
custom nn
micro macro
TorchVision
torchvision.transforms
Augmentation
Image Augmentation
Cut Effects
Image Augmentation
Smoothing
Label Smoothing
Regularized Training
compare
darknet53
AlexNet
vgg
googleNet
Inception Modules
resnet
ResNet Model
yolo
minirocket
lab simple minirocket demo
Pre-Defined Models
pytorch Inference
sota - state-of-the-art
zoo(s)
Pre Trained Computer Vision Models
Transfer Learning
compare methods
Fine-Tuning
Freeze Layers
Transfer Learning of resnet
Transfer overfit
lab reduce demo
transformers
lab transform basic
adaptation
visualize and debug
Object Location
pre-trained model for object localization
Localization
Object Localization
Detection
Transforms v2: End-to-end object detection/segmentation example
Common Object Detector Architecture
Non Maximum Suppression (NMS)
Mean Average Precision (mAP)
WorkShop - Object Detection with Yolov8
label example
yoloWRKSHP box formats
segmentation workshop
intro
data
models
UNET
the task
Repository
Open issue
.md
.pdf
pytorch for CV
pytorch for CV
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