Core Machine Learning Definitions: Algorithms and System Design
Core Machine Learning Concepts and Algorithms
Essential Definitions in Machine Learning
- Machine Learning: A field of AI where systems learn patterns from data to make predictions or decisions without being explicitly programmed.
- Find-Specific Hypothesis Algorithm: A concept learning algorithm that finds the most specific hypothesis consistent with the training examples.
- Concept Learning Task: The task of inferring a Boolean-valued function from training examples of inputs and outputs.
- Multilayer Network:
Deep Learning Fundamentals: GANs, Activation Functions, and Advanced RL
Generative Adversarial Networks (GANs)
A deep learning model featuring two competing neural networks (NNs) — the Generator and the Discriminator — designed to create realistic synthetic data.
(Mnemonic: The Generator creates fake data, and the Discriminator identifies it as real or fake.)
GAN Data Flow
Noise → Generator → Fake Data → Discriminator → Real/Fake
Core Components of GANs
- Generator: Creates synthetic data, attempting to fool the Discriminator (G).
- Discriminator: Evaluates data input,
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.,
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