Mastering Machine Learning Algorithms Second Edition

Mastering Machine Learning Algorithms Second Edition

Machine learning is a subset of artificial intelligence that aims to make modern-day computer systems more intelligent. The real power of machine learning lies in its algorithms, which make even the most difficult things capable of being handled by machines. Mastering Machine Learning Algorithms, Second Edition helps you harness the…

Machine Learning Algorithms – Second Edition

The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published today and will be soon available through all channels. From the back cover: Machine learning has gained tremendous popularity for its powerful and fast predictions through large datasets. However, the true forces behind its powerful…

Mastering Machine Learning Algorithms

Today I’ve published my latest book “Mastering Machine Learning Algorithms” (in a few days it will be available on all channels). From the back cover: Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides…

A glimpse into the Self-Organizing Maps (SOM)

Self-Organizing Maps (SOM) are neural structures proposed for the first time by the computer scientist T. Kohonen in the late 1980s (that’s why they are also known as Kohonen Networks). Their peculiarities are the ability to auto-cluster data according to the topological features of the samples and the approach to…

ML Algorithms addendum: Passive Aggressive Algorithms

Passive Aggressive Algorithms are a family of online learning algorithms (for both classification and regression) proposed by Crammer at al. The idea is very simple and their performance has been proofed to be superior to many other alternative methods like Online Perceptron and MIRA (see the original paper in the…

A Brief (and Comprehensive) Guide to Stochastic Gradient Descent Algorithms

Stochastic Gradient Descent (SGD) is a very powerful technique, currently employed to optimize all deep learning models. However, the vanilla algorithm has many limitations, in particular when the system is ill-conditioned and could never find the global minimum. In this post, we’re going to analyze how it works and the…

Linearly Separable? No? For me it is! A Brief introduction to Kernel Methods

This is a crash-introduction to kernel methods and the best thing to do is starting with a very simple question? Is this bidimensional set linearly separable? Of course, the answer is yes, it is. Why? A dataset defined in a subspace Ω ⊆ ℜn is linearly separable if there exists a (n-1)-dimensional hypersurface…

Hopfield Networks addendum: Brain-State-in-a-Box model

The Brain-State-in-a-Box is neural model proposed by Anderson, Silverstein, Ritz and Jones in 1977, that presents very strong analogies with Hopfield networks (read the previous post about them). The structure of the network is similar: recurrent, fully-connected with symmetric weights and non-null auto-recurrent connections. All neurons are bipolar (-1 and…