Tag: algorithms

Mastering Machine Learning Algorithms

Artificial Intelligence, Convnet, Deep Learning, Keras, Machine Learning, Machine Learning Algorithms Addenda, Neural networks, Python, Scikit-Fuzzy, Scikit-Learn, Tensorflow, TensorflowNo Comments

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 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. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the […]

Fundamentals of Machine Learning with Scikit-Learn

Artificial Intelligence, Machine Learning, Python, Scikit-LearnNo Comments

A tutorial video (2 hours) derived from the book Machine Learning Algorithms has been released: Fundamental of Machine Learning with Scikit-Learn: From the notes: As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge. In this course 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, […]

Getting Started with NLP and Deep Learning with Python

Artificial Intelligence, Deep Learning, Keras, Machine Learning, Neural networks, NLP, Python, Scikit-Learn, TensorflowNo Comments

A tutorial video (2 hours) derived from the book Machine Learning Algorithms has been released: Getting Started with NLP and Deep Learning with Python. From the notes: As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this course, you’ll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you’ll learn about Deep learning and TensorFlow. Finally, you’ll see how to create an Ml architecture. ISBN: 9781789138894 Link to the publisher page: https://www.packtpub.com/big-data-and-business-intelligence/getting-started-nlp-and-deep-learning-python-video Machine Learning Algorithms – Giuseppe Bonaccorso My […]

ML Algorithms addendum: Passive Aggressive Algorithms

Artificial Intelligence, Generic, Machine Learning, Machine Learning Algorithms Addenda, Python, Scikit-Learn4 Comments

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 reference section). Classification Let’s suppose to have a dataset: The index t has been chosen to mark the temporal dimension. In this case, in fact, the samples can continue arriving for an indefinite time. Of course, if they are drawn from same data generating distribution, the algorithm will keep learning (probably without large parameter modifications), but if they are drawn from a completely different distribution, the weights will slowly forget the previous one and learn the new distribution. For simplicity, we also assume we’re working with a binary classification based on bipolar labels. Given a weight vector w, the prediction […]

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

Artificial Intelligence, Deep Learning, Generic, Machine Learning, Machine Learning Algorithms Addenda, Neural networksNo Comments

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. In this context, we assume that Stochastic Gradient Descent operates on batch sizes equal or greater than 1. In particular, if we define the loss function for a single sample as: where x is the input sample, y the label(s) and θ is the parameter […]

An annotated path to start with Machine Learning

Artificial Intelligence, Deep Learning, Generic, Keras, Machine Learning, Neural networks, Python, Scikit-Learn, Tensorflow, Theano2 Comments

“Do not worry about your difficulties in Mathematics. I can assure you mine are still greater.” (A. Einstein)   Machine Learning is becoming more and more widespread and, day after day, new computer scientists and engineers begin their long jump into this wonderful world. Unfortunately, the number of theories, algorithms, applications, papers, books, videos and so forth is so huge to disorient whoever hasn’t a clear picture of what he wants/needs to learn to improve his/her skills. In this short post, I wanted to share my experiences, suggesting a feasible path to learn quickly the essential concepts and being ready to go deeper the most complex topics. Of course, this is only a personal proposal: every student can choose to dedicate more attention to some topics which are more interesting based on his/her experience. Prerequisites Machine Learning is based on Mathematics. It’s not an optional, theoretical approach: it’s a fundamental pillar […]