Reinforcement learning is an appealing subject. Firstly, it is a very general concept: an agent interacts with an environment with the goal to maximize the rewards it receives from the environment. The environment is random and provides states and rewards to the agent, while the agent chooses actions according to a possibly random policy. The… Continue reading Lecture notes on reinforcement learning
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Algorithms with Julia
This book provides an introduction to modern topics in scientific computing and machine learning, using JULIA to illustrate the efficient implementation of algorithms. In addition to covering fundamental topics, such as optimisation and solving systems of equations, it adds to the usual canon of computational science by including more advanced topics of practical importance. In… Continue reading Algorithms with Julia
Tutorial paper on Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic that has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This resource… Continue reading Tutorial paper on Deep Learning for Graphs
Dynastic Potential Crossover Operator
An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane… Continue reading Dynastic Potential Crossover Operator
Real-World Data Science Projects Management
Real-World Data Science Projects involve the practical application of data science methodologies to solve real-world problems. These projects require interdisciplinary collaboration, deal with large and complex datasets, and encompass the entire project lifecycle from data collection to deployment. Ethical considerations and advanced analytics techniques are also key aspects of these projects.
Gray Box Optimization
In Gray Box Optimization, the optimizer is given access to the set of M subfunctions. We prove Gray Box Optimization can efficiently compute hyperplane averages to solve non-deceptive problems in time. Bounded separable problems are also solved in time. As a result, Gray Box Optimization is able to solve many commonly used problems from the… Continue reading Gray Box Optimization