Вернуться к Introduction to Deep Learning

4.6

Оценки: 869

•

Рецензии: 194

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....

автор: 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.

автор: 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!

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Рецензии: 195

автор: Mohammed Saad Elsayed

•Apr 19, 2019

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

автор: 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.

автор: 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.

автор: Swapnil Kumar Bishnu

•Apr 11, 2019

One of the best courses on deep learning . Kudos to the creators.

автор: Tina Zhu

•Apr 07, 2019

A few typos in the slides, quizzes and in the homework, some of the presenters do not speak very clearly and are hard to follow (which would not be a big problem if they practiced their lectures, cleaned up the transcripts, gave out notes or powerpoint slides.) Quality of the course is much lower than the Stanford ML course on this site.

Coursera Jupyter notebooks keep disconnecting and my computer has trouble training the computation-heavy homework as well. Some of the homework is literally 95% wait for the computer or Coursera notebook to run or restart, 5% actual coding. It makes homework incredibly slow and inefficient for learning. I really want to learn the material and the lecturers are clearly very knowledgeable, but this course has some clear problems.

автор: Yu Qinyuan

•Apr 07, 2019

Brief but clear lessons for intermediate level students with not-easy assignments. :)

автор: Andrea Costa

•Mar 30, 2019

Very good content and top notch exercises. But sometimes the lectures are not fully comprehensible without a lot of additional reading from other sources.

автор: RLee

•Mar 26, 2019

A very comprehensive "introduction" course!

автор: Tolga Karahan

•Mar 22, 2019

It's one of the best courses I take about Deep Learning. Of course there are some issues as there are in all courses, but they are minimal. I got very important insights from this course and instructors know subjects well. Throughout the course I never suspected about it and money I spended.

автор: Simon Grützner

•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!

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