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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.
A clear and intuitive explanation of how contextual embeddings work, why they replaced static vectors like Word2Vec, and how they allow Transformers and LLMs to understand meaning dynamically based on context.
A clear and intuitive explanation of how Transformers work, why they eliminated recurrence, how they leverage massive parallelization, and how they became the foundation of today’s language models like GPT, Claude, Gemini, and Llama.
A clear and intuitive explanation of how attention works, why it replaced recurrent architectures, and how it became the foundation of Transformers and modern language models.
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