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Introduction to Deep Learning, Université nationale de recherche, École des hautes études en sciences économiques

4.6
754 notes
170 avis

À propos de ce cours

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....

Meilleurs avis

par 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!

par 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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170 avis

par Igor Buzhinskii

Feb 08, 2019

The course indeed gives an introduction to deep learning, but the practical part is discouraging since the "deep learning" part of practical assignments is usually given rather than asked to develop individually.

par Гридасов Илья Игоревич

Feb 07, 2019

The best course that I've ever seen. It gives wide and deep understanding of whatever in deep learning. I strongly recommended this course to you.

par Eric

Feb 05, 2019

An advanced class for overview for deep learning. A very wide range of the usages will let you think what you have learnt.

par Alfonso Medela

Jan 31, 2019

Good course.

par Ramin Anushiravani

Jan 21, 2019

Overall I enjoyed the course, but it lacks structure. Some materials are assumed to be well known by the learner and surprisingly some easier ones are not. I like to see the math, but it needs more materials to support it. Most instructor's have very heavy accent and tend to speak too quickly, I find myself rewinding multiple times just to figure out what was being said. Homework's are not too difficult, and are enjoyable. Except for the last one where you need to wait for a peer review. I think this can be a flagship course with more efforts.

par Dmitry

Jan 18, 2019

Alexander Panin has ruined this course with his pronunciation

P.S. finished the course with honors

par 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

par Chi Ezeh

Jan 13, 2019

I love the material!!

par Andreas Born

Jan 10, 2019

great course - I learned a lot!

par 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.