Understanding math will make you a better engineer.
So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.
Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?
If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.
Join the early access and be a part of the journey!
Math explained, as simple as possible.
Every concept is explained step by step, from elementary to advanced. No fancy tricks and mathematical magic. Intuition and motivation first, technical explanations second.
Open up the black boxes.
Machine learning is full of mysterious black boxes. Looking inside them allows you to be a master of your field and never be in the dark when things go wrong.
The roadmap
This is what is covered in detail
Linear algebra
Vector spaces
Structure of vector spaces: norms and inner products
Linear transformations and their matrices
Eigenvectors and eigenvalues
Solving linear equation systems
Special matrices and their decomposition
Calculus
Function limits and continuity
Differentiation
Minima, maxima, and the derivative
Basics of gradient descent
Integration
Multivariable calculus
Partial derivatives and gradients
Minima and maxima in multiple dimensions
Gradient descent in its full form
Constrained optimization
Integration in multiple dimensions
Probability theory
The mathematical concept of probability
Distributions and densities
Random variables
Conditional probability
Expected value
Information theory and entropy
Multidimensional distributions
Statistics
Fundamentals of parameter estimation
Maximum likelihood estimation
The Bayesian viewpoint of statistics
Bias and variance
Measuring predictive performance of statistical models
Multivariate methods
Machine learning
The taxonomy of machine learning tasks
Linear and logistic regression
Fundamentals of clustering
Principal Component Analysis
Most common loss functions and what’s behind them
Regularization of machine learning models
t-distributed stochastic neighbor embedding
Neural networks
Logistic regression, revisited
Activation functions
Computational graphs
Backpropagation
Loss functions, from a neural network perspective
Weight initialization
Advanced optimization
Stochastic gradient descent
Adaptive methods
Accelerated schemes
The Lookahead optimizer
Ranger
Convolutional networks
The convolutional layer, in-depth
Dropout and BatchNorm
Fundamental tasks of computer vision
Alexnet and Resnet
Autoencoders
Generative Adversarial Networks
Want to find out more?
Listen to Practical AI’s interview with Tivadar about the book!
Practical AI 152: The mathematics of machine learning – Listen on Changelog.com
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