Machine Learning Fundamentals: Algorithms and PyTorch Implementation
Machine Learning Core Concepts
Basic Mathematical Information
Matrix Multiplication
- If Matrix A has size (m x n) and Matrix B has size (n x p), the resulting product AB has size (m x p).
- The number of columns in A must equal the number of rows in B.
- Calculation involves multiplying each row of A by each column of B.
Finding Log Base 2 (n)
Key Machine Learning Definitions
- Supervised Learning: Models learn from labeled data to approximate a target function (hypothesis function).
- Classification: Goal is to
Machine Learning & AI Foundations: Definitions, Lifecycle, and Tools
CRISP-ML(Q) Project Lifecycle
- Definition: A 6-phase framework for managing machine learning projects, with a focus on quality at each step.
- Phases and Examples:
Business & Data Understanding
- Definition: Define the business problem and assess available data.
- Example: Goal: Reduce customer churn by 15%. Data: Purchase history, support tickets.
Data Preparation
- Definition: Clean, organize, and transform raw data for modeling.
- Example: Create “age” from “date of birth”; unify country codes like “USA” and
8085 Microprocessor Architecture & Assembly Language Fundamentals
Bus Organization in 8085 Microprocessors
A bus in the 8085 microprocessor is a group of wires used for communication between different components. There are three main types:
- Data Bus: Carries actual data, like a delivery van.
- Address Bus: Carries the memory address to access data, like a GPS.
- Control Bus: Carries control signals (e.g., read/write instructions). These signals coordinate data movement between the CPU, memory, and I/O devices.
Memory Addressing & Mapping Fundamentals
Memory addressing
Read MoreNLP Foundations: From Text Processing to Large Language Models
Week 1: Working with Words
Tokenization:
Splitting text into discrete units (tokens), typically words or punctuation . Techniques vary (simple split on whitespace vs. Advanced tokenizers); challenges include handling punctuation, contractions, multi-
word names, and different languages (e.G., Chinese has no spaces). Good tokenization is foundational for all NLP tasks.Bag-of-Words (BoW):
Representing a document by the counts of each word in a predefined vocabulary, ignoring order . The vocabulary is
Cybersecurity Essentials: Concepts & Best Practices
Vulnerability & Patch Management
(Domain 4 Concepts)
Common Vulnerability Scoring System (CVSS)
- Rates vulnerabilities on a scale of 0-10 for severity.
Exploit vs. Zero-Day
- Exploit: A known attack method against a vulnerability.
- Zero-Day: A vulnerability that has no patch available yet.
Remediation vs. Mitigation
- Remediation: Completely fixing a vulnerability.
- Mitigation: Applying temporary protections while awaiting a full fix.
Common Scanning Tools
- Nessus / OpenVAS: Identify known vulnerabilities.
- Nikto:
Quantitative Methods & Machine Learning Essentials
Likelihood Function
The likelihood function describes how observed data depends on the model parameters, θ. It is often denoted as p(x|θ).
Maximum Likelihood Estimation (MLE)
The Maximum Likelihood Estimator (MLE), δ(x), is defined as:
or equivalently:
The Gaussian log-likelihood is shown above.
Asymptotic Distribution of MLE
When n is large, the asymptotic distribution of the MLE is given by:
where:
Examples include:
- Gaussian:
- Exponential:
Bayesian Estimation
The Bayesian forecast is given by:
Assume the
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