Category: Theory
Basics of Natural Language Processing with NLTK
A key element of Artificial Intelligence, Natural Language Processing is the manipulation of textual data through a machine in order to “understand” it, that is to say, analyze it to obtain insights and/or generate new text. In Python, this is most commonly done with NLTK.
Overview of Deep Reinforcement Learning
Reinforcement Learning is one of the most exciting parts of Machine Learning and AI, as it allows for the programming of agents taking decisions in both virtual and real-life environments. This MIT course presents the theoretical background as well as the actual Deep Q-Network algorithm, that power some of the best Reinforcement Learning applications.
Computer vision and convolutional neural networks
Computer vision is a key aspect of artificial intelligence that is critical to many applications, from robots movements to self-driving cars and from medical imaging to products recognition in manufacturing plants. This MIT course presents the issues of computer vision and how they are handled with Convolutional Neural Networks together with the latest domains of…
Conceptual and mathematical summary for machine learning
Machine learning makes use of multiple mathematical formulas and relations to implement the different tasks it can handle. Gathered in the following “cheat sheets” by Afshine and Shervine Amidi, the concepts for supervised and unsupervised learning, deep learning together with machine learning tips and tricks, probabilities, statistics algebra and calculus reminders, are all presented in details…
Catalogue of neural networks architectures
Neural networks come in a wide range of shapes and functions, with diverse architectures and parameters for input, hidden and output nodes as well as convolutive or recurrent nodes. Regrouped in a convenient summary by Fjodor Van Veen, the most popular architectures for neural networks have been cataloged with detailed descriptions for each type of…