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Introduction to Deep Learning, National Research University Higher School of Economics

4.6
920 ratings
201 reviews

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....

Top reviews

By SS

Jul 20, 2018

Fantastic course.In fact, I think it,s not a easy thing to accomplish all the assignments with this course.\n\nI got a lot of gains through this course. Thanks for all the instructors.

By RK

Mar 01, 2019

Really Great course. I would recommend everyone to take this course but after having some "basic knowledge" of Machine Learning, Deep Learning, CNN, RNN and programming in python.

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202 Reviews

By SABYASACHI BHATTACHARYA

May 25, 2019

some lectures can be given at a slower pace

By Dalton Hall

May 24, 2019

This course was great. I thought the lectures were good, and the quizzes are good at testing your knowledge, but the bets part of the course comes from the assignments. The assignments were both fun and interesting, and allowed me to try different tasks I would have been too intimidated to try otherwise (such as GANs). I really enjoyed this course.

By Murat Öztürkmen

May 21, 2019

It is a well prepared course which includes lots of tips and trick and theoretical background to be successful.

By ashesh gajanan mishra

May 09, 2019

Its much more informative than the title suggests. A good course to take for someone who already knows basics/theoretical knowledge of machine learning.

By Jun Kunikata

May 02, 2019

Some programming assignments were not instructed enough, so it's very hard to solve them without discussion forums. But this is good course as a whole.

By Driaan Jansen

Apr 29, 2019

The content of the course is really excellent, and the lecturers' knowledge is just superb.

The only drawback of the course is that the lecturers' native language is not English, and accordingly it is sometimes difficult to understand them. But there are subtext to the lectures in English that one can refer to.

By AGWU Elbby Skermine

Apr 27, 2019

Learned and liked a lot

By Mohammed Saad Elsayed

Apr 19, 2019

very detailed , clear and to the point , i loved it

By Erik Grabljevec

Apr 13, 2019

This course gives a great overview of what can be done with DNNs. Topics are well chosen, clearly presented, and a good level of difficulty.

By Marian Lobur

Apr 12, 2019

I'm not sure that this course is needed at all. Folks are trying to explain multiple architectures of Neural Networks, without giving an actual understanding why it works. Plus I have a feeling that all of this things are going to explained in next courses of this specialization.