[MXML-2-01] Decision Trees [1/11] - History, Overview of ID3/C4.5 and CART

(17:39)

[MXML-2-02] Decision Trees [2/11] - ID3/C4.5, Impurity, Gini index, Entropy, Information Gain

(18:43)

[MXML-2-05] Decision Trees [5/11] - CART, Categorical Features, Label/One-Hot encoding

(14:12)

[MXML-8-02] Random Forest [2/7] - Random Forest from scratch, RandomForestClassifier

(16:38)

[MXML-2-06] Decision Trees [6/11] - CART, Information Gain (IG), Best Split Point

(17:1econd)

[MXML-2-08] Decision Trees [8/11] - CART, Feature Importance, Optimal tree depth

(13:56)

[MXML-2-09] Decision Trees [9/11] - CART, Cost Complexity Pruning (CCP)

(24:6)

How to Effectively Access and Control Flex Components Across MXML Pages

(2:21)

[MXML-2-07] Decision Trees [7/11] - CART, Decision Tree from scratch, using recursion

(25:10)

José Madero - MCMLXXX (Video Oficial)

(4:34)

LTJ Bukem - Earth Volume Two (1997)

(50:1econd)

Video 1: Introduction to Simple Linear Regression

(13:29)

EfficientML.ai Lecture 11 - TinyEngine and Parallel Processing (MIT 6.5940, Fall 2023, Zoom)

(1:16:)

Combining LLMs with Knowledge Bases to Prevent Hallucinations // Scott Mackie // LLMs in Prod Con 2

(43:43)

EfficientML.ai Lecture 10 - MCUNet: TinyML on Microcontrollers (MIT 6.5940, Fall 2023)

(1:46)

Lamiral x 2v x VT - 3M

(3:5)

Using ExLLaMaV2 library for fast LLM inference

(6:11)

2023 LLVM Dev Mtg - Arcilator: Fast and cycle-accurate hardware simulation in CIRCT

(25:59)

Stacia 2 Expert - #01

(3:28:11)

[MXML-9-02] AdaBoost [2/4] - Implementation of Binary Classification, Modified Algorithm to y={0,1}

(18:30)

[MXML-2-04] Decision Trees [4/11] - ID3/C4.5, Regression Tree, SDR, MSE, CV

(12:39)

[MXML-3-02] Linear Regression [2/7] - Overfitting and Regularization, LASSO and Ridge

(16:12)

[MXML-2-10] Decision Trees [10/11] - CART, Implement Pruning using CCP, Multiclass Classification

(13:12)

[MXML-2-11] Decision Trees [11/11] - CART, Decision Tree Regressor from scratch, using recursion

(15:24)

Auto Renaming of Open/Close MXML Tag

(23)

[MXML-5-02] Convex Optimization [2/4] - How to solve the EQP and IQP using Lagrange Method

(14:28)

[MXML-12-02] Light GBM [2/5] - Gradient-based One-Side Sampling (GOSS)

(14:40)

[MXML-4-02] Logistic Regression [2/5] - Implementing Logistic Regression, check the various results

(15:11)