Udemy - Adversarial ML - Attack and Defend Neural Networks

Adversarial ML: Attack & Defend Neural Networks

https://WebToolTip.com

Published 9/2026
Created by Bayt Al Hikmah
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 112 Lectures ( 25h 24m ) | Size: 1.2 GB

What you'll learn
⚡ Build a full PyTorch training and evaluation pipeline with reproducibility discipline
⚡ Implement the core white-box attack arsenal from scratch
⚡ Simulate realistic black-box threats against a live FastAPI inference service
⚡ Attack beyond evasion
⚡ Engineer real defenses and measure them honestly
⚡ Build ML governance evidence
⚡ Automate MLOps and supply-chain security
⚡ Deploy and operate production inference safely
⚡ Design for sovereignty
⚡ Deliver a capstone-grade Sovereign Adversarial ML Defense Platform

Requirements
❗ Knowledge: Basic Python (functions, running scripts) and basic terminal comfort. No prior deep learning, PyTorch, or adversarial ML experience required — Module 1 trains your first neural network from a bare workstation up. Basic familiarity with machine learning concepts (what training and accuracy mean) helps but isn't required — every concept is explained before it's used. No prior Kubernetes, MLOps, or DevSecOps experience needed — Modules 8–9 build those skills from scratch. Software (all free/open-source): Python 3.11+, Git, Docker. Python packages installed via pip in Lab 1: PyTorch, torchvision, scikit-learn, FastAPI, Uvicorn, pytest, MLflow, the Adversarial Robustness Toolbox (ART), Foolbox, Evidently, Great Expectations, Prometheus client, OpenTelemetry — all free and open-source. kubectl (optional, for Module 9's Kubernetes labs) — a local cluster, not a cloud account. Optional: Trivy and Cosign for supply-chain labs — the course provides graceful fallbacks if they aren't installed. Hardware: CPU-only is fully sufficient — this course intentionally uses a small, fast scikit-learn digits dataset (8x8 grayscale images) so every lab runs in seconds without a GPU. 5GB+ free disk space, 4GB+ RAM. No cloud account, no GPU, and no real production data required — every lab uses a safe, built-in benchmark dataset with no licensing or download concerns.

Description
This course contains the use of artificial intelligence.