Artificial Intelligence Lab
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.
Continue reading “Basics of Natural Language Processing with NLTK”
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 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 research and state-of-the-art algorithms architectures.
Continue reading “Computer vision and convolutional neural networks”
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 with the underlying math.
Based on the Stanford course on Machine Learning (CS 229), the cheat sheets summarize the important concepts of each branch with simple explanations and diagrams, such as the following table cover underfitting and overfitting.
| Underfitting | Just right | Overfitting | |
| Symptoms | • High training error • Training error close to test error • High bias | • Training error slightly lower than test error | • Very low training error • Training error much lower than test error • High variance |
| Regression illustration |  |  |  |
| Classification illustration |  |  |  |
| Deep learning illustration |  |  |  |
| Possible remedies | • Complexify model • Add more features • Train longer | • Perform regularization • Get more data |
The main machine learning cheat sheets can be found here:
Other mathematics and coding cheat sheets can be found here:
The complete cheat sheets can also be found on Github.
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 neural network. The complete post with explanations on the use and goals of each network can be be found on the Asimov Institute “the neural network zoo“.
