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Introducción a los encofrados y las cimbras en obra civil y edificación
Managing Conflicts on Projects with Cultural and Emotional Intelligence
Introducción a la Ciencia de Datos con Python
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Explore how computers predict and generate sequences using Recurrent Neural Networks. Learn about vectors, neural networks, and practical applications in cooking, weather, and more.
Introducción práctica al aprendizaje automático en español, cubriendo técnicas supervisadas y no supervisadas con aplicaciones reales como reconocimiento de imágenes y sistemas de recomendación.
Explore Latent Dirichlet Allocation through a two-part series, covering its fundamentals and training using Gibbs Sampling.
Explore Bayes Theorem, Hidden Markov Models, Shannon Entropy, Naive Bayes classifier, Beta distribution, and Thompson sampling in this friendly introduction to key probability concepts.
Explore denoising autoencoders, VAEs, GANs, and RBMs in this comprehensive introduction to generative models, covering key concepts and applications in machine learning.
Explore key unsupervised learning techniques including clustering, dimensionality reduction, and generative models. Gain insights into real-world applications like recommendation systems and image compression.
This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.
Explore key machine learning concepts, algorithms, and evaluation methods in this comprehensive introduction to the field.
Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.
Explore linear regression, logistic regression, perceptron algorithm, and support vector machines in this friendly introduction to key supervised learning concepts.
Demystifying Transformer models with visuals and examples, covering key concepts like attention mechanism, tokenization, embeddings, and fine-tuning for various NLP tasks.
Explore Direct Preference Optimization (DPO), an efficient method for training Large Language Models without reinforcement learning. Learn about the Bradley-Terry model, KL divergence, and loss function.
Demystify attention mechanisms in Transformer models through visuals, examples, and mathematical explanations, focusing on embeddings, similarity, and key-query-value matrices.
Explore the visual explanation of Bessel's correction in variance estimation, understanding its importance and mathematical foundations for accurate statistical analysis.
Explore binomial and Poisson distributions, from basic concepts to practical applications in probability calculations for real-world scenarios.
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