Word2Vec Semantics and Technology
Word2vec is a method to efficiently create embeddings developed in 2013. Apart of word-embedding, some of its concepts have also proved effective in developing recommendation engines and data interpretation, even in regards of commercial, non-language tasks. You can see that all modern NLP applications are based on Word2vec algorithms. Today we are looking into Word2Vec technology along with the methods for knowledge representation in intelligent systems. Watch this video on youtube.com
Keras. Convolutional Neural Network
A convolutional neural network (also CNN or ConvNet) is one of the most common deep learning algorithms. It's a class of machine learning in which a model learns to classify objects directly in images, video, audio or text.
Today we will find out what features make convolutional neural networks so useful. In the practical part, we will train them to understand geometric relations and test them. Watch this video on youtube.com
Support Vector Machine (SVM)
Today's seminar is dedicated to one of the popular supervised learning methods, which is used to solve classification and regression problems. The algorithm, also known as the maximum-margin classifier method, is widely used for solving both linear and nonlinear problems.
Its main idea is to create an optimally separating hyperplane for the sampled objects. Watch this video on youtube.com
Handwritten Character Recognition
The object recognition in image data is a popular illustration of the power of deep learning techniques.
Today, you will learn how to create a deep learning model for the MNIST handwritten digit recognition problem in Python using the Keras deep learning library. Watch this video on youtube.com
How We See What We See?
Let's talk about how eyesight works in regards of psychology and neurophysiology.
Our special guest is Dr Leonid Savchenko, a Senior Research Associate at the Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, London. Watch this video on youtube.com
Classical Recognition Methods
Today, we will look into the recognition methods in machine learning. We will pay attention to probabilistic methods, recall the formula for Bayes' theorem, and study the paradoxes of n-dimensional spaces. We will also review the methods for density estimation and algorithms testing. Watch this video on youtube.com