Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

Today I've published my latest book "Mastering Machine Learning Algorithms" (in a few days it will be available on all channels). 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 in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms.

I am an Artificial Intelligence Software Engineer and Data Scientist working in AI, Machine Learning, Deep Learning, and Enterprise Projects Design and Delivery. I was involved in several projects with the following technologies: C/C (11 & 14), Python, Java/J2EE, Artificial Intelligence, Machine and Deep Learning (Scikit-Learn, Tensorflow, Theano, Keras), R, Big Data, Hadoop, Spark. My main interests include Machine Learning, Deep Learning, Reinforcement Learning, Convolutional Networks and Sequence Modeling, Bio-inspired adaptive systems, self-organizing models and Neural Language Processing. I've been working in the following business contexts: Public administrations, Utilities, NATO/military organizations, Healthcare Informatics, Online advertising, and B2C services. If you're interested in my freelance services, please get in touch through the contact form.

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 most important variations that can speed up the convergence in deep models. First of all, it's necessary to standardize the naming. In some books, the expression "Stochastic Gradient Descent" refers to an algorithm which operates on a batch size equal to 1, while "Mini-batch Gradient Descent" is adopted when the batch size is greater than 1.

My latest machine learning book has been published and will be available during the last week of July. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems.