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

4.6
707 ratings
164 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 YG

Jan 28, 2018

This is a very hands on Deep Learning class. Like the design of programming assignments a lot. It's very instructive as well as challenging! Great course. I would recommend it!

By AS

Mar 26, 2018

Great course! The faculty does an excellent job in explaining some difficult to understand concepts. The discussion forum is very active and the course community is helpful.

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165 Reviews

By Dmitry

Jan 18, 2019

Alexander Panin has ruined this course with his pronunciation

P.S. finished the course with honors

By Isaiah Onando Mulang'

Jan 15, 2019

The course compels you to work on the solutions and hence expose you to hand-on that are very vital for understanding

By Chi Ezeh

Jan 13, 2019

I love the material!!

By Andreas Born

Jan 10, 2019

great course - I learned a lot!

By Emanuel Pinheiro Fontelles

Jan 09, 2019

It is not a introductory course! The course provides an excellent path showing the most tools in deep learning techniques but you have to spend more time looking for additional material to supplementary this course. In general you will learn the basic about Neural Networks, Convolutional Neural Networks, and Natural Language Processing.

By Milos Vlainic

Jan 08, 2019

Interesting and useful course. Capstone project was quite difficult, but I learned a lot - so I do not want to complain about it. Maybe a bit more code-related things during the lectures would be useful to make capstone project easier.

By Yaran Jin

Jan 06, 2019

Good overview of deep learning topics like CNN and RNN, and also hands on coding assignment of Tensorflow. However, this is a big gap between the video material and the programming assignment. Need to add more training for Tensorflow before deep learning models. And the instructors speak too fast.

By 胡哲维

Dec 23, 2018

excellent

By Darya Loseva

Dec 14, 2018

In general the course is good, it gives you the idea of different neural networks, their usage and a bit of their inner math. The only thing I didn't really like: most programming assighnments contain large precoded parts, which are difficult to understand. For me it would be more useful, if assighnments wouldn't be so difficult, but I had to code myself.

By Alexander

Dec 13, 2018

Tell more about TensorFlow and Keras. It was hard to finish final project due to lack of the knowledge in that area.