
MLG 008 Math
Échec de l'ajout au panier.
Veuillez réessayer plus tard
Échec de l'ajout à la liste d'envies.
Veuillez réessayer plus tard
Échec de la suppression de la liste d’envies.
Veuillez réessayer plus tard
Échec du suivi du balado
Ne plus suivre le balado a échoué
-
Narrateur(s):
-
Auteur(s):
À propos de cet audio
Try a walking desk to stay healthy while you study or work!
Full notes at ocdevel.com/mlg/8
Mathematics in Machine Learning- Linear Algebra: Essential for matrix operations; analogous to chopping vegetables in cooking. Every step of ML processes utilizes linear algebra.
- Statistics: The hardest part, akin to the cookbook; supplies algorithms for prediction and error functions.
- Calculus: Used in the learning phase (gradient descent), similar to baking; it determines the necessary adjustments via optimization.
- Recommendation: Learn the basics of machine learning first, then dive into necessary mathematical concepts to prevent burnout and improve appreciation.
- MOOCs: Khan Academy - Offers Calculus, Statistics, and Linear Algebra courses.
- Textbooks: Commonly recommended books for learning calculus, statistics, and linear algebra.
- Primers: Short PDFs covering essential concepts.
- The Great Courses: Offers comprehensive video series on calculus and statistics. Best used as audio for supplementing primary learning. Look out for "Mathematical Decision Making."
- Tensor: General term for any dimension list; TensorFlow from Google utilizes tensors for operations.
- Efficient computation using SimD (Single Instruction, Multiple Data) for vectorized operations.
- Gradient descent used for minimizing loss function, known as convex optimization. Recognize keywords like optimization in calculus context.
Ce que les auditeurs disent de MLG 008 Math
Moyenne des évaluations de clientsÉvaluations – Cliquez sur les onglets pour changer la source des évaluations.
Il n'y a pas encore de critiques pour ce titre.