Voltar para Introduction to Deep Learning

4.6

estrelas

1,267 classificações

•

282 avaliações

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.
Do you have technical problems? Write to us: coursera@hse.ru...

Sep 20, 2019

one of the excellent courses in deep learning. As stated its advanced and enjoyed a lot in solving the assignments. looking forward for more such courses especially in Natural language processing

Jun 02, 2019

one of the best courses I have attended. clear explanation, clear examples, amazing quizzes & Programming Assignment this course is advanced level, don't enroll it if you are a new starter.

Filtrar por:

por Yuanxin W

•Mar 17, 2019

As a junior deep learning research intern, I feel this course is a good refresher for some dl knowledge and applications. One suggestion I would have is the instructors should have more explanations on the math part (Jacobian matrix etc..). Overall quality is great!

por Alfredo G Z

•Dec 29, 2019

I definitely learnt a lot, this is called Intro to Deep Learning yet it is much more than an Intro.

Assignments are hard but challenge you to think a lot.

The only thing I would improve is if the staff did more maintaining and responded more quickly to the forums.

por Pavan K U

•Aug 27, 2019

Overall course content is good and engaging. But I feel a little bit gap while doing assignments. I feel instructions are clear but for better understanding, it would have been better if a sample input and output is present after every function we implement.

por Vladimir S

•Dec 13, 2017

Курс немного сыроват. Особенно касается задания для отличников на одной из недель, которое нельзя сабмитить. А так молодцы - курс хороший хоть и с недочетами. СПС авторам.

З.Ы. Некоторым лекторам всё-таки я бы посоветовал поработать над произношением...

por Lee C H

•Oct 08, 2018

The assignments of this course are very challenging but rewarding. It's not very 'INTRO' as stated in the course title as the pacing is super fast (one type of NN per week!). Overall a highly recommended course to anybody who's interested !

por Diego T B

•Sep 28, 2019

I think this is a nice course! however I felt like I cannot do many of the things I did in the course from scratch. Nice topics, well taught. The only bad point is that subtitles in English are terrible, they need to be more accurate.

por Ануфриев С С

•Sep 12, 2018

Good, however in programming assignments could be beneficial to add mathematical equations for computations or maybe add variables input. By doing so, much less confusion with shapes and computational workflow could be achieved.

por Gökhan T

•Oct 08, 2018

it's really hard to compare other introduction courses, it's tough course. You need to hard word to complete. It enjoyable course, there are alot to learn and it will teach you some key features of deep learning.

por Голубев К О

•Aug 15, 2018

Great course with nice lecturers and intersting practice. Of course I'd like to grasp more theoretical and practical questions from this course (GANs, Prizma, e. t. c.), but everything should have it's own end

por nishan p

•Feb 24, 2018

A good course for introducing the various concepts of Deep Learning. It neither goes too deep into the models nor taught in a blackbox way. I think it's just a mixed of both ways. The teachers were good. :)

por dinesh k

•Sep 20, 2019

one of the excellent courses in deep learning. As stated its advanced and enjoyed a lot in solving the assignments. looking forward for more such courses especially in Natural language processing

por Alexandru C

•Oct 26, 2019

It is quite a rare case, but the course is quite challenging, in a good way. I would definitely not recommend you to start machine learning with it, but it is a good course to advance

por superfantastic

•Jul 20, 2018

Fantastic course.In fact, I think it,s not a easy thing to accomplish all the assignments with this course.

I got a lot of gains through this course. Thanks for all the instructors.

por Yiwei G

•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!

por Akash S

•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.

por Frank F

•Jul 31, 2018

Finally a Course that does more than introducing the topic, but helps you in your every practice of deep learning modelling. Awesome! Looking forward to much more to come!

por Timo

•May 30, 2019

Definitely great course!

(Was more interested in the math than the acutal "doing" of the programming, so I sometimes found the exercises a little enerving... :) )

por Jens R

•Sep 02, 2019

I learned a lot. I had a tiny typo in the last exercise, which took most of my time. But searching for this mistake was probably the time I learnt the most ;).

por Hamel H

•Dec 29, 2017

This is amazing content. The instructors have a really good sense of humor which you can detect if you are paying attention, this makes the course really fun.

por Simon G

•Mar 20, 2019

I really liked, that you are able to clone the repositories directly do work locally on the notebooks and therefore providing a much more stable environment!

por Saket G

•Jun 17, 2018

Challenging and motivating, it is not self sufficient but its ok to see some resources on Internet.Always excited to study this.Thanks to all teachers...!!

por Zhanpeng H

•Jan 05, 2018

This is the best course that I have taken so far about deep learning on Coursera. It contains nice explanations about different types of neural networks.

por Hussein N

•Nov 03, 2019

I really enjoyed this course and how practical it is. It was super exciting to make the a practical application with transfer learning only after 4 weeks

por ashesh g m

•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.

por Pun C S

•Oct 18, 2019

Quite In depth introduction on Deep learning. But you need to have a solid background on python and machine learning in order to catch up the materials

- IA para todos
- Introdução ao TensorFlow
- Redes neurais e aprendizagem profunda
- Algoritmos, parte 1
- Algoritmos, parte 2
- Aprendizagem Automática
- Aprendizagem automática com Python
- Aprendizagem automática usando o Sas Viya
- Linguagem R
- Introdução à programação com Matlab
- Análise de dados com Python
- Fundamentos da AWS: Going Cloud Native
- Fundamentos da Google Cloud Platform
- Engenharia de confiabilidade do site
- Fale inglês profissionalmente
- A ciência do bem-estar
- Aprendendo a Aprender
- Mercados Financeiros
- Testes de hipóteses em saúde pública
- Princípios da liderança no cotidiano

- Aprendizagem profunda
- Python para todosPython para todos
- Ciência de Dados
- Ciência de dados aplicada com Python
- Fundamentos de negóciosFundamentos dos Negócios
- Arquitetura com o Google Cloud Platform
- Engenharia de dados em Google Cloud Platform
- Excel para MySQL
- Aprendizagem de máquina avançada
- Matemática para aprendizagem automática
- Carros autoguiáveis
- Revolução do Blockchain para a empresa
- Análises empresariaisAnálises Empresariais
- Habilidades em Excel para negócios
- Marketing digitalMarketing Digital
- Análise estatística com R para saúde pública
- Fundamentos da imunologia
- Anatomia
- Gestão da inovação e Design Thinking
- Princípios da psicologia positiva