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Brett Van Tussler's Applied Concepts and Technical Dictionaries

website·Indexed 30 days ago

https://brett-van-tussler.github.io

A curated collection of notes, code examples, and technical glossaries spanning bioinformatics, machine learning, mathematics, and general computer science. Content includes tutorials on pandas, the Python requests library, SQL window functions, and LeNet5, alongside specialized dictionaries for machine learning and bioinformatics.

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What's inside Brett Van Tussler's Applied Concepts

  1. Overview of Convolutional Neural Networks (CNN)

    A Convolutional Neural Network (CNN) is a neural network architecture inspired by the visual cortex. It utilizes sliding filters to automatically learn and detect features in grid-like data, making it specifically effective for image-based tasks such as facial recognition, object detection, and medical image analysis.

  2. Overview of Deep Learning

    Deep Learning is a subset of machine learning utilizing multiple layers of neural networks. It is specifically effective for complex tasks involving images, text, and speech, powering models such as ChatGPT and stable diffusion.

  3. Understand Generative Adversarial Networks (GANs)

    A GAN is an AI architecture consisting of two competing neural networks: a generator that creates synthetic data and a discriminator that attempts to distinguish between real and fake data. This adversarial process drives the generator to produce increasingly realistic synthetic data, used in AI art and deepfakes.

  4. Implement Automatic Differentiation with Autograd

    Autograd (automatic differentiation) is a computational technique used to calculate exact derivatives of functions defined by computer programs. It is fundamental for training neural networks via gradient-based optimization in frameworks like PyTorch, TensorFlow, and JAX.

    Autograd works by tracking operations on tensors to build a computational graph. During the backward pass, it traverses this graph in reverse order and applies the chain rule to compute gradients of loss functions with respect to model parameters. While it supports both forward-mode and reverse-mode differentiation, reverse-mode is typically used in backpropagation because it is more efficient for functions with many inputs and few outputs.

  5. Use Deno as a JavaScript/TypeScript runtime

    Deno is a secure runtime for JS and TS. Key developer features include:

    • Security by Default: Requires explicit permissions for file, network, and environment access.
    • Built-in TS Support: No additional configuration needed.
    • URL-based Imports: No package.json or external package manager required.
    • Integrated Tooling: Includes a built-in formatter, linter, test runner, and bundler.
    • Web-standard APIs: Uses modern JavaScript features and web-standard APIs.

  6. Understand the Transformer architecture

    The Transformer is a neural network architecture that uses self-attention mechanisms to process sequential data. This allows the model to better understand context within language and serves as the foundation for modern LLMs like GPT and BERT.

  7. Establish shared secrets via Diffie-Hellman Key Exchange

    Diffie-Hellman is a cryptographic protocol that allows two parties to establish a shared secret key over an insecure channel without a prior shared secret. It relies on modular exponentiation and the difficulty of the discrete logarithm problem. It is a fundamental component of secure protocols such as HTTPS, SSH, IPsec, and TLS.

  8. Understand SQL Window Function basics and syntax

    Window functions perform calculations across a set of table rows related to the current row. Unlike aggregate functions, they do not group rows into a single output row; each row retains its separate identity. The set of rows the function operates on is called a 'window' and is defined by the OVER clause.
    function_name([arguments]) OVER (
    [PARTITION BY column1, column2, ...]
    [ORDER BY column1 [ASC|DESC], column2 [ASC|DESC], ...]
    [ROWS|RANGE frame_specification]
)

  9. Understand how LLMs compress training data into model parameters

    LLMs use high-dimensional vector spaces to encode information as patterns and abstractions rather than verbatim memorization. This allows models to be significantly smaller than their training corpora. The compression is achieved through:

    • Pattern Recognition: Encoding statistical patterns and relationships between concepts instead of individual facts.
    • Distributed Representations: Spreading concept information across millions of parameters to efficiently encode overlapping concepts.
    • Parameter Sharing: Reusing the same parameters across different contexts and tasks.
    • Embeddings: Representing words and concepts as dense vectors where semantic relationships are mapped as geometric relationships (clustering similar concepts).

  10. Use Long Short-Term Memory (LSTM) for sequential data

    LSTM is an advanced Recurrent Neural Network (RNN) architecture designed to solve the "vanishing gradient" problem. By using specialized gates to control information flow, LSTMs can remember important information over longer sequences, making them suitable for language translation and speech recognition.

  11. Utilize Tensor Processing Units (TPUs)

    TPUs are Google-developed ASICs optimized for matrix operations and tensor operations fundamental to deep learning. They provide higher computational throughput and lower precision than CPUs or GPUs, significantly reducing training time for large-scale models (like Gemini) and improving inference efficiency in production.

  12. ANSI SQL Standard Overview

    ANSI SQL is the American National Standards Institute standard for Structured Query Language, ensuring portability across relational database management systems (RDBMS). It defines standard syntax for:

    • Data Definition Language (DDL): CREATE, ALTER, DROP.
    • Data Manipulation Language (DML): SELECT, INSERT, UPDATE, DELETE.
    • Data Control Language (DCL): GRANT, REVOKE.
    • Transaction Control: COMMIT, ROLLBACK.
    • Operations: Standard aggregate functions (SUM, COUNT, AVG), Join operations (INNER, LEFT, RIGHT, FULL OUTER), and Common Table Expressions (CTEs).