Algorithms and Data Structures

"Compare yourself with who you were yesterday"

Every Sturday I join LeetCode Weekly Contest and improve coding skill by solving coding problems. I know there are a lot of better coders in the world but I compare myself who I was yesterday to move forward.

Dynamic Programming
• 1105. Filling Bookcase Shelves [Dynamic Programming]
• 1039. Minimum Score Triangulation of Polygon
• 1036. Escape a Large Maze
• 1035. Uncrossed Lines
• 1000. Minimum Cost to Merge Stones
Binary Tree
• 104. Maximum Depth of Binary Tree
• 1026. Maximum Difference Between Node and Ancestor
• 617. Merge Two Binary Trees
• 998 Smallest String Starting From Leaf
Linked List
• 1019. Next Greater Node In Linked List

Machine Learning

"The best way to learn is to explain"

Even if we can use them, we do not fully understand the things. I explain the things I used for my daily job as well as the ones that I would like to learn.

Preprocessing
• Normalizing Observations
• One-Hot Encode Nominal Categorical Features
• Convert Strings To Dates
• Parse HTML
• Dimensionality Reduction With PCA
Visualization
• Introduction to Bayesian Optimization
• Draw Perceptron graph by graphviz
• Parallel Plot for Cateogrical and Continuous variable by Plotly Express
• Split Up: dtreeviz (Part 5)
• Split Up: dtreeviz (Part 4)
• Split Up: dtreeviz (Part 3)
• Visualization Samples by Plotly Express
• Split Up: dtreevis (Part 2)
• Feature Importance
• Getting real-time stock market data and visualization
• XKCD-style Plot using matplotlib
• Split-Up: dtreeviz (Part 1)
• Data Exploration Tool - Lantern Part 1
• Introduction to Graphviz in Jupyter Notebook
Image
• Explain the prediction for ImageNet using SHAP
• Explain Image Classification by SHAP Deep Explainer
• Interpretability of Random Forest Prediction for MNIST classification using LIME
• Save Images
• Train the image classifier using PyTorch
• Loading scikit-learn's MNIST Hand-Written Dataset
• Style Transfer using Pytorch (Part 4)
• Style Transfer using Pytorch (Part 3)
• Style Transfer using Pytorch (Part 2)
• Style Transfer using Pytorch (Part 1)
Text
• BERT Word Embeddings
• Tokenize Text
• Remove Punctuation
• Bag Of Words
• Term Frequency Inverse Document Frequency
• Parse HTML
• Create a word cloud
Time Series
• Time Series Forecasting for Daily Births Dataset by Prophet
• Prophet 101: a time-series forecasting module
• Loading Audio Data
• Getting real-time stock market data and visualization
Interpretability
• Explain the interaction values by SHAP
• Explain the prediction for ImageNet using SHAP
• Sentiment Analysis by SHAP with Logistic Regression
• Explain Iris classification by SHAP
• Explain Image Classification by SHAP Deep Explainer
• Interpretability of prediction for Boston Housing using SHAP
• Interpretability of Random Forest Prediction for MNIST classification using LIME
Deep Learning
• Explain Image Classification by SHAP Deep Explainer
• Loss Functions in Deep Learning with PyTorch
• 3 ways of creating a neural network in PyTorch
• How to Develop a 1D Generative Adversarial Network From Scratch in PyTorch (Part 1)
• Anomaly Detection by Auto Encoder (Deep Learning) in PyOD
• Train the image classifier using PyTorch
• Activation Functions in Neural Networks
• Style Transfer using Pytorch (Part 4)
• Style Transfer using Pytorch (Part 3)
• Style Transfer using Pytorch (Part 2)
• Style Transfer using Pytorch (Part 1)
• PyTorch Basic Operations