Machine Learning Algorithms: Use Cases and Compute Requirements
Machine Learning Algorithm Comparison
| Algorithm | Train or Test Data | Use Cases | Supervised | Pipe? | File Type | CPU or GPU |
|---|---|---|---|---|---|---|
| AutoGluon-Tabular | Training and (optionally) validation | High-accuracy tabular prediction achieved through automated ensembling and multi-layer stacking. | Y | N | CSV | CPU or GPU (single instance only, M5) |
| BlazingText | Train | Text classification for use cases such as sentiment analysis, spam detection, and hashtag prediction. | Y | Y | Text file (one sentence per line with space-separated tokens) | CPU or GPU (single instance |
LLM Cheatsheet: Essential Concepts and Architectures
LLM Cheatsheet: Essential Concepts
Everything you need to recall about Large Language Models (LLMs) — summarized on one page.
What is a Large Language Model (LLM)?
A Large Language Model (LLM) is a deep learning model trained on vast amounts of text data to understand, generate, and manipulate human language.
- Examples: GPT-4, Claude, LLaMA, Gemini, Mistral, Falcon
- Core Technology: The Transformer Architecture
LLM Core Components
| Component | Description |
|---|---|
| Tokenizer | Converts text into tokens (subwords, words) |
Natural Language Processing Core Concepts and Techniques
Text Preprocessing and Feature Space Reduction
Which of the following text preprocessing steps can reduce the dimensionality of a bag-of-words feature space?
- A. Converting all text to lowercase
- B. Removing common stop words (e.g., “the”, “and”, “of”)
- D. Stemming or lemmatizing words (e.g., “running” → “run”)
Limitations of Bag-of-Words Representation
Which of the following are limitations of the bag-of-words (unigram) text representation?
- A. It ignores the order of words in the text
Integrating SQL, Understanding NoSQL, and MongoDB Fundamentals
Database Programming: Integrating SQL with Host Languages
Introduction to SQL Integration
This section covers how SQL is used in host languages like C and Java, utilizing techniques such as Embedded SQL, Dynamic SQL, JDBC, and SQLJ. It demonstrates essential steps like connecting to a database, declaring host variables, retrieving data using cursors, and executing dynamic queries.
Embedded SQL (C Example)
Embedded SQL allows direct integration of SQL statements within a host language program (e.g.,
Read MoreMachine 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