Artificial Intelligence: The Complete Foundation Guide

Key Definitions:

• Machine Learning: Algorithms that improve through experience
• Deep Learning: Neural networks with multiple layers
• NLP: Natural Language Processing (ChatGPT, Bard)
• Computer Vision: Image/video understanding

Real-World Examples:

• GPT-4: 1.7 trillion parameter LLM
• Tesla Autopilot: Vision-based driving
• AlphaFold: Protein structure prediction

Technical Insight:

Modern AI systems use transformer architectures with self-attention mechanisms

Learning Methods:

• Supervised Learning: Labeled datasets (Image classification)
• Unsupervised Learning: Pattern discovery (Customer segmentation)
• Reinforcement Learning: Reward-based (AlphaGo)

Training Process:

# Simplified PyTorch training loop
for epoch in range(epochs):
    model.train()
    for batch in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

Data Requirements:

ImageNet: 14M labeled images | Common Crawl: 250B web pages

Key Components:

• GPUs: NVIDIA H100 (700 TFLOPS)
• TPUs: Google's custom AI accelerators
• Neuromorphic Chips: Brain-inspired architectures

Performance Metrics:

Chip	TFLOPS	Memory	Use Case
NVIDIA A100	312	80GB	Large model training
Google TPUv4	275	32GB	Cloud inference