Articles in category "Flower Book"

Study Notes on Deep Learning III — Numerical Computation

2018-01-15 193 views Deep Learning Flower Book Deep Learning Numerical Calculation

This article mainly explores some key concepts in the fields of deep learning and optimization, including gradient, partial derivative, constrained optimization, and the KKT method. Below is the organization and summary of these contents: ### 1. Gradient and Partial Derivative - **Univariate Function**: For a univariate function $ f(x) $, the stationary point (extreme point) can be found by solving its derivative $ df/dx = 0 $. - **Multivariate Function**: - **Partial Derivative**: For a function with multiple inputs $ z = f(x, y) $, partial derivatives can be computed by differentiating with respect to each input separately.

Study Notes on "Deep Learning" - Part 2: Probability Theory

2018-01-14 175 views Deep Learning Flower Book Deep Learning Probability Theory

This document covers many important concepts in probability theory and machine learning, including the distributions of random variables, commonly used functions, and correlation coefficients. Below is a summary of some key content: ### 1. Random Variables and Probability Distributions - **Bernoulli Distribution**: The distribution of a single binary random variable. - **Multinoulli Distribution (Categorical Distribution)**: The distribution over a single discrete random variable with $ k $ distinct states. - **Gaussian Distribution (Normal Distribution)**: \[ \mathcal{N}(x \]

Study Notes on Deep Learning I — Linear Algebra

2018-01-13 213 views Deep Learning Flower Book Deep Learning Linear Algebra

This note covers various important concepts in machine learning, particularly those related to linear algebra. Below are some summaries and supplements to the content of the note: ### Fundamentals of Linear Algebra 1. **Matrices and Vectors**: Introduces matrices (arrays composed of multiple rows and columns) and vectors (essentially matrices with a single column or row). Emphasizes their importance in machine learning. 2. **Linear Combinations and Span**: - Linear Combination: Represented as $\sum_i x_i{\bf A}_{:,i}$. - Span (Note: The original content was cut off, so this is an assumption based on the context. If there was more specific content, please provide it for accurate translation.)