๐ฟ๐ฎ๐บ๐ฎ๐ธ๐ฟ๐๐๐ต๐ป๐ฎโ ๐ฒ/๐ฎ๐ฐ๐ฐ
@techwith_ram โข 12,717 subscribers
Sr. DS. AI Updates. Views are my own. ๐ฅฆ https://t.co/k0P7ZvFN2M
Videos
This is a great lecture at MIT by David Shirokoff on Markov Chains. He covers the fundamentals of Markov Chains using a simple particle movement example. He starts by explaining how a particle moves between two positions, A & B, with different probabilities. From there, the talk converts the problem into matrix form using a Markov matrix. The main topics covered are: - Transition probabilities - Markov matrices - Probability vectors - Matrix multiplication in Markov Chains - Finding probabilities after n steps - Eigenvalues and eigenvectors - Matrix diagonalization - Long-term steady state distribution
๐ฟ๐ฎ๐บ๐ฎ๐ธ๐ฟ๐๐๐ต๐ป๐ฎโ ๐ฒ/๐ฎ๐ฐ๐ฐ139,686 views โข 16 days ago
This lecture was a really simple & practical introduction to how machines learn Bayesian Learning, Bayes Theorem, Naive Bayes from Kimia Lab by Professor H.R.Tizhoosh. The lecture also walks through - MAP - Maximum Likelihood - Bayes Optimal Classifier - Naive Bayes model A really beginner-friendly lecture if you want to understand how probability thinking shaped modern machine learning.
๐ฟ๐ฎ๐บ๐ฎ๐ธ๐ฟ๐๐๐ต๐ป๐ฎโ ๐ฒ/๐ฎ๐ฐ๐ฐ14,756 views โข 12 days ago
๐๐ผ๐ ๐๐ผ๐ป๐๐ผ๐น๐๐๐ถ๐ผ๐ป๐ฎ๐น ๐ก๐ฒ๐๐ฟ๐ฎ๐น ๐ก๐ฒ๐๐๐ผ๐ฟ๐ธ๐ ๐ช๐ผ๐ฟ๐ธ A Convolutional Neural Network (CNN) works by using layers to process images: โข Convolutional layers scan the input image using filters to detect features like edges, textures, and patterns.๐
๐ฟ๐ฎ๐บ๐ฎ๐ธ๐ฟ๐๐๐ต๐ป๐ฎโ ๐ฒ/๐ฎ๐ฐ๐ฐ41,750 views โข 1 year ago
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