d2l.ai, chapter 3.2 Object-Oriented Design for Implementation
d2l.ai, chapter 3.1 Linear Regression
Probablity and Statistics Covariance Matrix, Diagonal & Off-Diagonal Entries Covariance Matrix A covariance matrix \(\Sigma\) is a square matrix that shows the covariance between each pair of coordinates in a random vector.
2.4 Calculus 2.4.1 Derivatives and Differentiation 2.4.2 Visualization Utilities 2.4.3 Partial Derivatives and Gradients When there is no ambiguity, ∇ₓf(x) is typically replaced by ∇f(x). The following rules come in handy for differentiating multivariate functions:
2.2 Data Preprocessing 2.2.1 Reading the Dataset 2.2.2 Data Preparation inputs = pd.get_dummies(inputs, dummy_na=True) inputs = inputs.fillna(inputs.mean()) 2.2.3 Conversion To the Tensor Format from jax import numpy as jnp X = jnp.
1.3.2 UnSupervised and Self-Supervised Learning The author lists the difference between traditional software engineering and machine learning. While software engineering focuses on solving problems with rule-based solutions, machine learning(deep learning) tries to solve problems that are ambiguous and the solution can evlove by itself after seeing more examples.
colab 2.1.1 Get Started import jax from jax import numpy as jnp x = jnp.arange(12) x -> Array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=int32) x.
1.0 Introduction The author lists the difference between traditional software engineering and machine learning. While software engineering focuses on solving problems with rule-based solutions, machine learning(deep learning) tries to solve problems that are ambiguous and the solution can evlove by itself after seeing more examples.
Distilling Introduction, Chapter 13 - 20
📘 Solve the equation: \[ \sqrt{5 - x} = 5 - x^2 \] ✏️ Step 1: Eliminate the square root Square both sides: \[ (\sqrt{5 - x})^2 = (5 - x^2)^2 \] \[ 5 - x = 25 + x^4 - 10x^2 \] ✏️ Step 2: Rearrange into a standard polynomial form Move all terms to one side: