Day108 Deep Learning Lecture Review - Advanced Techniques in DL
HW5: Out-of-distribution (OOD) Detection (Maximum Softmax Probability & ODIN) and Continual Learning (SLDA & IID Streaming)
Category
TIL_24
Day107 Deep Learning Lecture Review - HW4 - Adjusting Probabilities into Real-World
HW4: Model Calibration (Platt Scaling & Label Smoothing) and Conformal Prediction (Naive and Adaptive Predictions Sets)
Day106 Deep Learning Lecture Review- Enhancing DL Workflows (Data Distribution)
HW3: Optimization through Data Loading, Profiling, & Scaling, and Comparison of Data Parallel & Distributed Data Parallel
Day105 Deep Learning Lecture Review - Lecture 20 (End)
Model Drifting, Periodic Re-Training, Detecting Model Drift, Continual Learning (Pre-Trained Model, NCC), and Real-Time Machine Learning
Day104 Deep Learning Lecture Review - Lecture 19
Data-Centric AI: Label Noise, Selection Bias, Data Leakage, and Error Analysis for Model Improvement (Subgroup Errors)
Day103 Deep Learning Lecture Review - Lecture 18 (2)
Data-Centric AI: Active Learning, SEALS(Similarity Search for Efficient Active Learning), Dataset Pruning, and Data Engine
Day102 Deep Learning Lecture Review - Lecture 18 (1)
Data-Centric AI: Crowdsourcing, Methods to Estimate Annotator Quality, Neural Scaling Laws, Pareto Curves and Power Law
Day101 Deep Learning Lecture Review - Lecture 17 (2)
Variation of Conformal Prediction: Size of Calibration Set, Evaluation, and Group-Based & Adaptive Conformal Prediction
Day100 Deep Learning Lecture Review - Lecture 17 (1)
Understanding Conformal Prediction: Concepts, Applications, Marginal Coverage, and Recipes In Detail
Day99 Deep Learning Lecture Review - Lecture 16
Uncertainty in Deep Learning, Distribution Shifts, Model Calibration, and Out-of-Distribution (OOD) Detection
Day98 DL Review - Revisiting LLM models (Hand-Written Tree Map)
Language Models- Transfer Learning, Basic Concepts & Terminologies, Components of NLP Models, and Attention Mechanism
Day97 Deep Learning Lecture Review - Lecture 15 (2)
Bias Mitigation Strategies: Loss Reweighting, Sampling & Synthetic Samples and Architectural Changes (OccamNets, Adversarial Training & DANN)
Day96 Deep Learning Lecture Review - Lecture 15 (1)
Model Comparison and Bias Mitigation; McNemar’s Test, Dataset Bias, and Bias Detection
Day95 Deep Learning Lecture Review - Lecture 14
AI Ethics; AI Safety, Key Issues, AGI (Artificial General Intelligence), and Current AI Models’ Challenges
Day94 Deep Learning Lecture Review - Lecture 13
Llama 3: Framework, Workflow (RMSNorm, Grouped Query Attention, RoPE, SwiGLU Attention), Pre-training & Post-training
Day93 Deep Learning Lecture Review - Fine-Tuning Models (2) & Prompt Engineering
Comparing Pre-trained model embeddings (ResNet+SBERT vs. CLIP) and Prompt Engineering (Short and Direct, Few-Shot Learning, & Expert Prompting)
Day92 Deep Learning Lecture Review - Fine-Tuning Models (1)
HW2: Understanding of LoRA and Pre-trained Model Embeddings (ResNet+SBERT) for Visual Question Answering (VQA)
Day91 Deep Learning Lecture Review - Optimizing Hyperparameters
Weights & Biases (W&B) for Monitoring and Fine-Tuning ResNet-18 and Post-Training Evaluations (Dying ReLU, Brightness Robustness)
Day90 Deep Learning Lecture Review - Background Knowledges
HW0: Softmax Properties, PyTorch Lightning, and DataLoader
Day89 ML Review - Ensemble Method (5)
Revisiting Ensemble Method, Random Forest, and XGBoost
Day88 Deep Learning Lecture Review - Lecture 10-12
Deep Learning & Numerical Precision(Floating Point), Hardware Considerations, and Distributed Model Training
Day87 Deep Learning Lecture Review - Lecture 8 (2) & 9
LLMs - Speeding Up LLMs (Grouped Query Attention, KV Caches, MoE, and DPO)
Day86 Deep Learning Lecture Review - Lecture 8 (1)
LLMs- Generating Texts, Positional Encoding, and Fine-Tuning LLMs (LoRA)
Day85 Deep Learning Lecture Review - Lecture 7
LLMs - Perplexity, Tokenizers, Data Cleaning, and Embedding Layer
Day75-84 Introduction to Natural Language Processing using Deep Learning
Basic Machine Learning & Deep Learning, Word Embedding, CNNs, RNNs, LSTM and Transformer
Day74 Deep Learning Lecture Review - Lecture 6
Large Language Model - BERT, GPT, and GPT-2, 3 & 4
Day73 Deep Learning Lecture Review - Lecture 5
Transformers and Foundation Models: GELU, Layer Norm, Key Concepts & Workflow
Day72 Deep Learning Lecture Review - Lecture 4
Brief Explanation of Basic Algebra and Machine Learning
Day71 DL Review - Natural Language Processing (NLP)
Primary Goals, Common Tasks, and Deep Learning NLP
Day70 DL Review - Transformer
Transformer Architecture, How the Models Are Different, and Q,K,V in Self-Attention
Day69 DL Review - Convolutional Neural Networks (CNNs)
Basic Concepts and the Detailed Architecture
Day68 Deep Learning Lecture Review - Lecture 3
Neural Net Zoo: Transformers, Recurrent Neural Networks (RNNs) and Graph Neural Networks (GNNs)
Day67 Deep Learning Lecture Review - Lecture 2-3
Types of Learning and Neural Net Zoo: Fully Connected Networks (MLPs), Inductive Bias, and Convolutional Neural Networks (CNNs)
Day66 Deep Learning Lecture Review - Lecture 1
Basic Mathematics, Supervised ML, and Review of Multi-Layered Perceptron
Day65 ML Review - Ensemble Method (4)
Bagging & Boosting : Basic Concepts & Code Implementation
Day64 ML Review - Ensemble Method (3)
Using the Majority Voting Principle to Make Predictions, and Evaluating & Tuning the Ensemble Classifier
Day63 ML Review - Ensemble Method (2)
Code Structure of Combining Classifiers via Majority Vote
Day62 ML Review - Ensemble Method (1)
Key Concepts and Mathematics Explanation
Day61 ML Review - Class Imbalances
Use Other Metrics, Assign Different Class Weights, or Upsample the Minority Class
Day60 ML Review - Cross Validation (5)
ROC area Under The Curve (ROC AUC)
Day59 ML Review - Cross Validation (4)
Confusion Matrix and F1 score
Day58 ML Review - Cross Validation (3)
Grid Search for Fine-Tuning Machine Learning Models
Day57 ML Review - Cross Validation (2)
Bias & Variance, and Learning & Validation Curves
Day56 ML Review - Cross Validation (1)
Model Selection and K-Fold Cross Validation
Day55 ML Review - Pipeline
Key Concepts and Example Code with Scikit-learn
Day54 ML Review - Dimensionality Reduction (5)
Applying Kernal Principal Component Analysis(KPCA) to New Data Points
Day53 ML Review - Dimensionality Reduction (4)
Implementing a Kernal Principal Component Analysis(KPCA) in Python
Day52 ML Review - Dimensionality Reduction (3)
Nonlinear Mappings with Kernel Principal Component Analysis
Day51 ML Review - Dimensionality Reduction (2)
Compressing Data via Linear Discriminant Analysis
Day50 ML Review - Dimensionality Reduction (1)
Compressing Data via Dimensionality Reduction and Summary of PCA
Day49 ML Review - Data Preprocessing (3)
Partitioning a Dataset into Training & Test Datasets, Feature Scaling, and Feature Selection
Day48 ML Review - Data Preprocessing (2)
Handling Categorical Data - Converting, Ordinal Encoding, and One-Hot Encoding
Day47 ML Review - Data Preprocessing (1)
Handling Missing Data - Eliminating and Imputing & Estimators API
Day46 ML Review - K-Nearest Neighbors (3)
The Curse of Dimensionality
Day45 ML Review - K-Nearest Neighbors (2)
Distance Metrics- Euclidean, Manhattan, Minkowski & Chebyshev Distance, and Cosine Similarity
Day44 ML Review - K-Nearest Neighbors (1)
Basic Concepts, How It Works, and Parametric & Non-Parametric Model
Statistics & Probability Lecture Review
Introduction of Statistics (2023) Whole Lecture Review
Day39-43 Linear Algebra & Matrix Review (Korean)
Linear Algebra & Matrix for Programmers
Day38 ML Review - Random Forest (2)
Implementation Step by Step
Day37 ML Review - Decision Tree (3) & Random Forest (1)
Building a Decision Tree & Random Forest (1) - Key Concepts & How it Works
Day36 ML Review - Decision Tree (2)
Information Gain (2) - Entropy & Classification Error
Day35 ML Review - Decision Tree (1)
Components, How it Works & Maximizing Information Gain (1) - Gini Impurity
Day34 ML Review - Support Vector Machine (3)
Solving Nonlinear Problems - Using a Kernal SVM
Day33 ML Review - Support Vector Machine (2)
SVM: Nonlinear Separable Case
Day32 ML Review - Support Vector Machine (1)
Basic Concepts and Mathematical Formulations
Day31 ML Review - Logistic Regression (3)
How To Train in Scikit-Learn, and Regularization with LR model
Day30 ML Review - Logistic Regression (2)
Cost Function of Logistic Regression
Day29 ML Review - Logistic Regression (1)
Basic Concepts and Sigmoid Function
Day28 ML Review - Perceptron (3)
Step by Step - Training Perceptron (3)
Day27 ML Review - Perceptron (2)
Step by Step - Training Perceptron (2)
Day26 ML Review - Perceptron (1)
Choosing a Classification Algorithm Step by Step - Training Perceptron (1)
Day25 Statistics Review (4)
Population Proportions, p-values & Confidence Intervals, and Type I & II Errors
Day24 Statistics Review (3)
Test Statistics (Z-Test, t-Test, and Chi-Squared Test)
Day23 Statistics Review (2)
Law of Large Numbers, Central Limit Theorem, and Hypothesis Testing (1) - General Setup
Day22 Statistics Review (1)
Properties of Random Variable
Day21 Probability Review (3)
Continuous Probability Distribution and Markov Chains
Day20 Probability Review (2)
Joint, Marginal, & Conditional Probability Distributions, and Discrete & Poisson Distributions
Day19 Probability Review (1)
Basic Probability - Counting and Random Variables
Day18 ML Review - Principle Component Analysis (3)
Applying PCA in Machine Learning and Scree Plot
Day17 ML Review - Regularization
Concepts, Types of Regularization, and How It Works
Day16 ML Review - R-squared (3)
Further Analysis on R-Squared
Day15 ML Review - R-Squared (2)
How R-Squared Is Used As a Performance Metric in Machine Learning
Day14 ML Review - R-Squared (1)
Concepts Overview, Mathematical Calculation, and Interpretation
Day13 ML Review - Gradient Descent (3)
Summarization and Types
Day12 ML Review - Gradient Descent (2)
Mathematical Explanation
Day11 ML Review - Gradient Descent (1)
Basic Concepts, Steps, and Key Consideration
Day10 ML Review - Gradient
Understanding Gradients in Machine Learning Applications
Day09 ML Review - Linear Regression (3)
SSE Sum Squared Error and Choosing Cost Function
Day08 ML Review - Linear Regression (2)
Cost Function and MSE Mean Squared Error
Day07 ML Review - Linear Regression (1)
Concepts Overview and Mathematical Calculation Exercise
Day06 ML Review - Principle Component Analysis (2)
Applications on Machine Learning & Further Explanations
Day05 ML Review - Principle Component Analysis (1)
Mathematical Definition & Algorithms
Day04 Basic Mathematics Review (4)
Bayesian Statistics, the Law of Total Probability, and Var-Cov Matrix
Day03 Basic Mathematics Review (3)
Eigendecomposition, Symmetric Matrix, and Eigendecomposition
Day02 Basic Mathematics Review (2)
Norm(2), Eigenvectors and Eigenvalues
Day01 Basic Mathematics Review (1)
Scalar, Norm, Matrix (Inverse, Basic Functions), Rank
TIL_25
Day175 - MLOps Review: Data Distribution Shifts And Monitoring (2)
Designing Machine Learning Systems: Causes of ML System Failures (2) (Correcting Degenerate Feedback Loops) & Data Distribution Shifts
Day174 - MLOps Review: Data Distribution Shifts And Monitoring (1)
Designing Machine Learning Systems: Causes of ML System Failures (1) (Production data differing from training data, Edge Cases, and Degenerate Feedback Loops)
Day173 - MLOps Review: Model Deployment And Prediction Service (3)
Designing Machine Learning Systems: ML on the Cloud and on the Edge & Model Optimization (AutoTVM & WebAssembly)
Day172 - MLOps Review: Model Deployment and Prediction Service (2)
Designing Machine Learning Systems: Model Comparison (Low-Rank Factorization, Knowledge Distillation, Pruning, & Quantization)
Day171 - MLOps Review: Model Deployment and Prediction Service (1)
Designing Machine Learning Systems: Model Deployment and Batch Prediction Versus Online Prediction (Unifying Batch and Streaming Pipeline)
Day170 - MLOps Review: Model Development and Offline Evaluation (4)
Designing Machine Learning Systems: Model Offline Evaluation (Methods: Perturbation Tests, Invariance Tests, etc.)
Day169 - MLOps Review: Model Development and Offline Evaluation (3)
Designing Machine Learning Systems: Auto ML (Hyperparameter Tuning & NAS), Model Offline Evaluation (1) (Establishing Baselines)
Day168 - MLOps Review: Model Development and Offline Evaluation (2)
Designing Machine Learning Systems: Experiment Tracking, Versioning, and Distributed Training (Data Parallel)
Day167 - MLOps Review: Model Development and Offline Evaluation (1)
Designing Machine Learning Systems: Model Selection, Evaluating ML Models, & Ensemble Method (Bagging, Boosting, and Stacking)
Day166 - MLOps Review: Feature Engineering (3)
Designing Machine Learning Systems: Data Leakage (Definition, Common Causes, Detecting and Preventing it)
Day165 - MLOps Review: Feature Engineering (2)
Designing Machine Learning Systems: Feature Engineering Techniques (2) (Positional Embeddings) & Engineering Good Features
Day164 - MLOps Review: Feature Engineering (1)
Designing Machine Learning Systems: Feature Engineering Techniques (1) (Handling Missing Values, Scaling, Normalization, Binning, Encoding Categorical Values...
Day163 - MLOps Review: Training Data (4)
Designing Machine Learning Systems: Data Augmentation (Simple Label-Preserving Transformations, Perturbation, and Data Synthesis)
Day162 - MLOps Review: Training Data (3)
Designing Machine Learning Systems: Class Imbalance (How to Deal with the problems: Evaluation Metrics, Over & Undersampling, Resampling and Algorithm-le...
Day161 - MLOps Review: Training Data (2)
Designing Machine Learning Systems: Labeling (Hand Labels, Natural Labels, & Addressing the Lack of Labels - Active Learning, etc.)
Day160 - MLOps Review: Training Data (1)
Designing Machine Learning Systems: Sampling (Nonprobability, Simple Random, Stratified, Weighted, Reservoir, and Importance Sampling)
Day159 - MLOps Review: Data Engineering Fundamentals (2)
Designing Machine Learning Systems: Modes of Dataflow & Batch / Real-Time Processing
Day158 - MLOps Review: Data Engineering Fundamentals (1)
Designing Machine Learning Systems: Data Formats (JSON, Parquet & Binary Format), Data Models (Relational & NoSQL), and Data Storage Engines (ETL)
Day157 - MLOps Review: Introduction to Machine Learning Systems Design (3)
Designing Machine Learning Systems: Framing ML Problems (2) (Types of ML Tasks & Objective Functions)
Day156 - MLOps Review: Introduction to Machine Learning Systems Design (2)
Designing Machine Learning Systems: Iterative Process & Framing ML Problems (1)
Day155 - MLOps Review: Introduction to Machine Learning Systems Design (1)
Designing Machine Learning Systems: Business and ML Objectives & Requirements for ML Systems
Day154 - STAT Review: Unsupervised Learning (7)
Practical Statistics for Data Scientists: Scaling and Categorical Variables (Scaling the Variables, Dominant Variables, Categorical Data, and Gower’s Distanc...
Day153 - STAT Review: Unsupervised Learning (6)
Practical Statistics for Data Scientists: Model-Based Clustering (Multivariate Normal Distribution, Mixtures of Normals & Selecting the Number of Cluster...
Day152 - STAT Review: Unsupervised Learning (5)
Practical Statistics for Data Scientists: Hierarchical Clustering (A Simple Example, the Dendrogram, the Agglomerative Algorithm & Measures of Dissimilar...
Day151 - STAT Review: Unsupervised Learning (4)
Practical Statistics for Data Scientists: K-Means Clustering (2) (Interpreting Clustering Results & Determining the Optimal Number of Clusters K)
Day150 - STAT Review: Unsupervised Learning (3)
Practical Statistics for Data Scientists: K-Means Clustering (1) (A Simple Example & K-Means Algorithm Code Source)
Day149 - STAT Review: Unsupervised Learning (2)
Practical Statistics for Data Scientists: Principal Components Analysis (2) (Formal Definition, Interpreting Components & Correspondence Analysis)
Day148 - STAT Review: Unsupervised Learning (1)
Practical Statistics for Data Scientists: Principal Components Analysis (1) (Unsupervised Learning, A Simple Example and Computing the Principal Components)
Day147 - STAT Review: Statistical Machine Learning (9)
Practical Statistics for Data Scientists: Boosting (2) (Regularization, Hyperparameters & Cross-Validation)
Day146 - STAT Review: Statistical Machine Learning (8)
Practical Statistics for Data Scientists: Boosting (1) (Key Concepts & XGBoost)
Day145 - STAT Review: Statistical Machine Learning (7)
Practical Statistics for Data Scientists: Bagging and the Random Forest (2) (Random Forest II & Variable Importance)
Day144 - STAT Review: Statistical Machine Learning (6)
Practical Statistics for Data Scientists: Bagging and the Random Forest (1) (Bagging and Random Forest)
Day143 - STAT Review: Statistical Machine Learning (5)
Practical Statistics for Data Scientists: Tree Models (3) (Dealing With Overfitting Problems in R and Python & Predicting a Continuous Value)
Day142 - STAT Review: Statistical Machine Learning (4)
Practical Statistics for Data Scientists: Tree Models (2) (A Simple Example, The Recursive Partitioning Algorithm, & Measuring Homogeneity or Impurity)
Day141 - STAT Review: Statistical Machine Learning (3)
Practical Statistics for Data Scientists: K-Nearest Neighbors (3) (KNN as a Feature Engine) & Tree Models (1) (Key Concepts)
Day140 - STAT Review: Statistical Machine Learning (2)
Practical Statistics for Data Scientists: K-Nearest Neighbors (2) (Standardization & Choosing K)
Day139 - STAT Review: Statistical Machine Learning (1)
Practical Statistics for Data Scientists: K-Nearest Neighbors (1) (Example, Distance Metrics, and One Hot Encoder)
Day138 - STAT Review: Classification (8)
Practical Statistics for Data Scientists: Strategies for Imbalanced Data (2) (Data Generation, Cost-Based Classification, and Exploring the Predictions)
Day137 - STAT Review: Classification (7)
Practical Statistics for Data Scientists: Strategies for Imbalanced Data (1) (Undersampling & Oversampling)
Day136 - STAT Review: Classification (6)
Practical Statistics for Data Scientists: Evaluating Classification Models (Confusion Matrix, ROC-AUC & Lift)
Day135 - STAT Review: Classification (5)
Practical Statistics for Data Scientists: Logistic Regression (3) Assessing the Model
Day134 - STAT Review: Classification (4)
Practical Statistics for Data Scientists: Logistic Regression (2) (GLM, Interpretation, Fitting the Model)
Day133 - STAT Review: Classification (3)
Practical Statistics for Data Scientists: Logistic Regression (1) (Mathematic Foundation: Odds, Logit Function, Formula, and Examples)
Day132 - STAT Review: Classification (2)
Practical Statistics for Data Scientists: Discriminant Analysis (Covariance, Discriminant Function, and Application: Predicting Default Risk)
Day131 - STAT Review: Classification (1)
Practical Statistics for Data Scientists: Naive Bayes (Theoretical Approach, Code Source, & Prediction)
Day130 - STAT Review: Regression and Prediction (8)
Practical Statistics for Data Scientists: Weighted Regression, and Interactions and Main Effects in Regression in Depth
Day129 - STAT Review: Regression and Prediction (7)
Practical Statistics for Data Scientists: Stepwise Regression & Model Selection in Depth
Day128 - STAT Review: Revisiting Mathematics Theories (4)
Mathematical Principles: Non-parametric Inference (Wilcoxon Signed-Rank Test & Wilcoxon Rank-Sum Test)
Day127 - STAT Review: Revisiting Mathematics Theories (3)
Mathematical Principles: Inference on Proportions- Sample Size Estimation, Hypothesis Testing, and Chi-Squared Test
Day126 - STAT Review: Revisiting Mathematics Theories (2)
Mathematical Principles: Inference for Variance, Chi-Squared Distribution, F-Statistics, and Inference on Proportions (Wald & Wilson)
Day125 - STAT Review: Revisiting Mathematics Theories (1)
Mathematical Principles: Hypothesis Testing, Paired Samples, Independent Samples, and additional concepts.
Day124 - STAT Review: Regression and Prediction (6)
Practical Statistics for Data Scientists: Partial Residual Plots and Nonlinearity, Polynomial & Spline Regression, and Generalized Additive Models
Day123 - STAT Review: Regression and Prediction (5)
Practical Statistics for Data Scientists: Regression Diagnostics- Outliers, Influential Observations, and Heteroskedasticity
Day122 - STAT Review: Regression and Prediction (4)
Practical Statistics for Data Scientists: Interpreting the Regression Equation - Correlation, Multicollinearity, Confounding Variables, and Interactions
Day121 - STAT Review: Regression and Prediction (3)
Practical Statistics for Data Scientists: Factor Variables in Regression, Dummy Variables, Many Levels, and Ordered Factor Variables
Day120 - STAT Review: Regression and Prediction (2)
Practical Statistics for Data Scientists: Assessing the Model, Cross Validation, Model Selection, and Prediction Using Regression
Day119 - STAT Review: Regression and Prediction (1)
Practical Statistics for Data Scientists: Simple Linear Regression, Least Squares, and Multiple Linear Regression
Day118 - STAT Review: Statistical Experiments and Significance Testing (5)
Practical Statistics for Data Scientists: Chi-Square Theories, Fisher’s Exact Test, Multi-Arm Bandit Algorithm, and Power & Sample Size
Day117 - STAT Review: Statistical Experiments and Significance Testing (4)
Practical Statistics for Data Scientists: ANOVA(One & Two-Way), F-statistic, and Chi-Square Test
Day116 - STAT Review: Statistical Experiments and Significance Testing (3)
Practical Statistics for Data Scientists: t-Tests, Multiple Testing, False Discovery, and Degrees of Freedom
Day115 - STAT Review: Statistical Experiments and Significance Testing (2)
Practical Statistics for Data Scientists: p-Values, Practical Applications, and Type I & Type II Errors
Day114 - STAT Review: Statistical Experiments and Significance Testing (1)
Practical Statistics for Data Scientists: A/B Testing, Hypothesis Tests (One-Way & Two-Way), and Permutation Test
Day113 - STAT Review: Data & Sampling Distributions (3)
Practical Statistics for Data Scientists: t-Dist, Binomial, Chi-Square, F-Dist, and Poisson Distribution.
Day112 - STAT Review: Data & Sampling Distributions (2)
Practical Statistics for Data Scientists: Bootstrap, Confidence Intervals, and Normal Distribution
Day111 - STAT Review: Data & Sampling Distributions (1)
Practical Statistics for Data Scientists: Sampling, Bias, and Sampling Distribution(Central Limit Theorem)
Day110 - STAT Review: Exploratory Data Analysis (2)
Practical Statistics for Data Scientists: Data Distribution, Correlation, and Various Data Visualization Plots
Day109 - STAT Review: Exploratory Data Analysis (1)
Practical Statistics of Data Scientists: Elements of Statistical Terminologies- Data Statistics Fundamentals, Data Types, and Estimates of Location & Var...
TIL_23
DataMiningPratice(4)-AggregatingData
Aggregating data with groupby
DataMiningPratice(3)-RefiningUnnecessaryColumns
Adding new columns and refining unnecessary columns.
DataMiningPratice(2)-RefiningDatasets
Change data type
DataMiningPratice(1)-BasicImportofDatasets
Utilize public data to import two completely different data, pre-process them, and merge them
Data Structure/ Ch03:BasicDataStructure(3)
Basic Data Structure - 3. Deque
Data Structure/ Ch03:BasicDataStructure(2)
Basic Data Structure - 2. Queue
Data Structure/ Ch03:BasicDataStructure(1)
Basic Data Structure - 1. Stacks
Data Structure/ Ch02:AlgorithmAnalysis(2)
TIL :CH02. Algorithm Anlaysis (2)
Data Structure/ Ch02:AlgorithmAnalysis
TIL: CH02. Algorithm Anlaysis
Data Structure/ Ch01:BasicPython
Python Basic Review
interview_Prep
(editing) Data Science Interview Prep - Statistics
Interview Questions & Answers
(editing) Data Science Interview Prep - SQL
Interview Questions & Answers
(editing) Data Science Interview Prep - ML Basic
Interview Questions & Answers
(editing) Data Science Interview Prep - LLM/AI
Interview Questions & Answers
(editing) Data Science Interview Prep - DL
Interview Questions & Answers
Notice
A New Chapter:: Continuing onward to MLOps
Designing Machine Learning Systems (MLOPs) Review Begins!
Continuing the TIL Project in 2025
The Ongoing Chronicles of TIL25 — A Motivating Expedition as a Data Scientist & AI/ML Engineer Candidate
The Start of TIL 24
The Start of Recording TIL Summer ‘24 - During Summer in Rochester as a Data Scientist Candidate