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MLG 008 Math
Manage episode 180982426 series 1457335
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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.
57 episodes
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Manage episode 180982426 series 1457335
Content provided by OCDevel. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by OCDevel or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
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.
57 episodes
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Similar to Machine Learning Guide
Redefining AI is the 2024 New York Digital Award winning tech podcast! Discover a whole new take on Artificial Intelligence in joining host Lauren Hawker Zafer, a top voice in Artificial Intelligence on LinkedIn, for insightful chats that unravel the fascinating world of tech innovation, use case exploration and AI knowledge. Dive into candid discussions with accomplished industry experts and established academics. With each episode, you'll expand your grasp of cutting-edge technologies and ...
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