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A clear and intuitive explanation of modern tokenization for LLMs: why word-level and character-level fail, how subwords solve both extremes, how BPE merges frequent pairs, how WordPiece optimizes likelihood, how SentencePiece handles multilingual text, and why tokenization affects cost, context length, and model quality
A clear and intuitive explanation of CLIP: shared latent spaces, contrastive learning, zero-shot classification, joint embeddings, and its role as the semantic engine behind Stable Diffusion, Midjourney, GPT‑4o Vision, Gemini, and modern multimodal AI.
A clear and intuitive explanation of Vision Transformers (ViT): 16×16 patches, embeddings, positional encoding, global attention, comparison with CNNs, modern variants (DeiT, Swin), and their foundational role in multimodal AI systems like CLIP, PaLI, Gemini, and GPT‑4o Vision
A clear and intuitive explanation of how CNNs work: convolution, padding, stride, pooling, 1×1 convolutions, major architectures (LeNet, AlexNet, VGG, Inception, ResNet, EfficientNet), and transfer learning in modern computer vision.
A clear and intuitive explanation of how modern language models (LLMs) work: tokenization, contextual embeddings, Transformer architecture, next‑token prediction, pretraining, RLHF, sampling, and multimodal capabilities.
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