Blog sobre temas del mundo Cloud, Tecnología, Videojuegos y algo mas!!!
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.
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.
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