Category: Artificial Intelligence

EphMrA 2019 Switzerland one day meeting

Artificial Intelligence, Data Science, Machine LearningNo Comments

I’m pleased to be a speaker in EphMrA 2019 one-day meeting in Basel, Switzerland (event page with all information) on September, 24th. My presentation will be: “Customer Jouney Optimization through Reinforcement Learning”. Even if the time slot is limited, I’ll do my best to show how it’s possible to employ innovative machine learning methods to optimize complex marketing processes and improve customer satisfaction. I invite everybody who lives or is in Basel to partecipate. All required information about the venue and the agenda are provided in the link above. Photo by Odin Aerni on Unsplash.   Recommendations and User-Profiling from Implicit Feedbacks – Giuseppe Bonaccorso 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.

Machine Learning Algorithms – Second Edition

Artificial Intelligence, Books, Convnet, Data Science, Deep Learning, Keras, Machine Learning, Machine Learning Algorithms Addenda, Neural networks, NLP, Python, Scikit-Learn, Spark, Tensorflow, TensorflowNo Comments

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 output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate sufficient insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across supervised, semi-supervised, and reinforcement learning areas. Once the core concepts of an algorithm have been exposed, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component […]

Recommendations and User-Profiling from Implicit Feedbacks

Artificial Intelligence, Convnet, Data Science, Deep Learning, Machine Learning, Neural networks, NLP, Scikit-Learn2 Comments

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 (age range, gender, interests, social interactions, and so on) or to the ratings they gave to specific items. The latter approach normally relies on explicit feedbacks (e.g. a rating from 0 to 10) which summarize the overall experience. Unfortunately, there are drawbacks to both cases. Personal data are becoming harder to retrieve and the latest regulations (i.e. GDPR) allow the user to interact with a service without the collection of data. Moreover, a reliable personal profile must be built using many attributes that are often hidden and can only be inferred using predictive models. Conversely, implicit feedbacks are easy to […]

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 […]

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

Artificial Intelligence, Computational Neuroscience, Convnet, Deep Learning, Generic, Machine Learning, Neural networks, Philosophy of Mind, Tensorflow, Tensorflow4 Comments

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 to the beach, about 25 years ago. It was the first time you made love to her. Now imagine a small animal (normally a prey, like a rodent) roaming around the forest and looking for food. A sound is suddenly heard and the rodent rises its head. Is it the pounding of water or a lion’s roar? We can skip the answer right now. Let’s only think about the ways an animal memory can work. Computer science drove us to think that memories must always be loss-less, efficient and organized like structured repositories. They can be split into standard-size slots […]

A glimpse into the Self-Organizing Maps (SOM)

Artificial Intelligence, Computational Neuroscience, Machine Learning, Machine Learning Algorithms Addenda, Neural networks, PythonNo Comments

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 the learning process. Contrary to methods like Gaussian Mixtures or K-Means, a SOM learns through a competitive learning process. In other words, the model tries to specialize its neurons so to be able to produce a response only for a particular pattern family (it can also be a single input sample representing a family, like a handwritten letter). Let’s consider a dataset containing N p-dimensional samples, a suitable SOM is a matrix (other shapes, like toroids, are also possible) containing (K × L) receptors and each of them is made up of p synaptic weights. The resulting structure is a tridimensional matrix W […]

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 […]