Blog sobre temas del mundo Cloud, Tecnología, Videojuegos y algo mas!!!
A clear and intuitive explanation of how LLMs generate text using Greedy Decoding, Beam Search, Temperature, Top-k, Top-p, and Repetition Penalty. Learn how models transform probability distributions into coherent responses.
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.
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