Artificial Intelligence Lab
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”
This series of articles dives deeper into the actual applications of Machine Learning that are currently in use in many current technological processes and devices.
Through these posts entitled “Machine Learning is Fun!”, Adam Geitgey guides us step by step through the concepts, data, algorithms, code, results and pitfalls of machine learning applications from image, face and speech recognition to language translation and more. It also gathers several different sources for more details on each application and its development.
This series is really dense with detailed code, but it is also explained very clearly, step by step, with detailed illustration. It notably covers the use of a Convolutional Neural Network (including Generative Adversarial Network) and Recurrent Neural Network, together with some of their most prominent applications in daily life. It is a real course not to be missed for any ML developer!
Here is the list of posts with direct links:
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“.