Second-Order Optimization Methods: Newton, Quasi-Newton, and L-BFGS for Machine Learning
Master second-order optimization methods for machine learning. Learn Newton's method, Quasi-Newton, …
Master second-order optimization methods for machine learning. Learn Newton's method, Quasi-Newton, …
Master linear algebra operations using NumPy with practical examples. Learn matrix operations, …
Master information theory for machine learning. Learn entropy, cross-entropy loss, KL divergence, …
Master tensors for deep learning with practical examples in NumPy, PyTorch, and TensorFlow. Learn …
Master covariance matrices, correlation analysis, and multivariate distributions for machine …
Master convex optimization fundamentals for machine learning. Learn convex functions, Lagrange …
Master statistical inference for machine learning. Learn hypothesis testing, p-values, confidence …
Master learning rate scheduling for deep learning. Implement warmup, step decay, cosine annealing, …
Master MLE and MAP estimation for machine learning. Learn when to use each, mathematical …
Master gradient descent optimizers with practical examples. Compare SGD, Momentum, Adam, RMSprop, …
Master Bayes' theorem for machine learning. Learn prior, posterior, likelihood with intuitive …
Master backpropagation with step-by-step derivations, computational graphs, and practical code …
Master probability distributions essential for ML. Learn Gaussian, Bernoulli, Poisson, and more with …
Master the calculus foundations for deep learning. Learn derivatives, gradients, partial …