Code Implementations, explanation of concepts for my Probabilistic Machine Learning Series. Hello folks, In this new coding demonstration, we code, and explain the concepts pertaining to: 1.Overfitting, Population Risk & Generalisation Gap. Proxy for Population Risks : Test Set. The No free Lunch Theorem and Inductive Biases. Unsupervised Learning : Density Estimation and Clustering. VAEs(Variational Autoencoder)- Latent factors concepts explained, and VAE architecture explained and coded. Self-Supervised Learning-Masked Predictions. 7.Density Evaluation and Sample Efficiency. Reinforcement Learning Primer : Multi-Armed Bandits. Implementation Link: https://youtu.be/gbz8smggmRM submitted by /u/Negative_War_65
Originally posted by u/Negative_War_65 on r/ArtificialInteligence