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…

Recommendations and User-Profiling from Implicit Feedbacks

Recommendations and Feedbacks The vast majority of B2C services are quickly discovering the strategic importance of solid recommendation engines to improve the conversion rates and an establish a stronger fidelity with the customers. The most common strategies are based [3] on the segmentation of users according to their personal features…

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…

Hetero-Associative Memories for Non Experts: How “Stories” are memorized with Image-associations

Think about walking along a beach. The radio of a small kiosk-bar is turned-on and a local DJ announces an 80’s song. Immediately, the image of a car comes to your mind. It’s your first car, a second-hand blue spider. While listening to the same song, you drove your girlfriend…

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…

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…