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.
Size: 1.26G
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.
Created by: National Research University Higher School of Economics
-
Taught by: Evgeny Sokolov, Senior Lecturer
HSE Faculty of Computer Science -
Taught by: Andrei Zimovnov, Senior Lecturer
HSE Faculty of Computer Science -
Taught by: Alexander Panin, Lecturer
HSE Faculty of Computer Science -
Taught by: Ekaterina Lobacheva, Senior Lecturer
HSE Faculty of Computer Science -
Taught by: Nikita Kazeev, Researcher
HSE Faculty of Computer Science
Basic Info |
Course 1 of 7 in the Advanced Machine Learning Specialization
|
Level | Advanced |
Commitment | 6 weeks of study, 6-10 hours/week |
Language |
English
|
How To Pass | Pass all graded assignments to complete the course. |
User Ratings |
4.5 stars
Average User Rating 4.5See what learners said
|