About this course: The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models.
Who is this class for: Developers, analysts and researchers who are faced with tasks involving complex structure understanding such as image, sound and text analysis.
Taught by: Evgeny Sokolov, Senior LecturerHSE Faculty of Computer Science
Taught by: Andrei Zimovnov, Senior LecturerHSE Faculty of Computer Science
Taught by: Alexander Panin, LecturerHSE Faculty of Computer Science
Taught by: Ekaterina Lobacheva, Senior LecturerHSE Faculty of Computer Science
Taught by: Nikita Kazeev, ResearcherHSE Faculty of Computer Science
Course 1 of 7 in the Advanced Machine Learning Specialization
|6 weeks of study, 6-10 hours/week
|How To Pass
|Pass all graded assignments to complete the course.
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