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....

创建者 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.

创建者 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!

筛选依据：

186 个审阅

创建者 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!

创建者 Prateek Gupta

•Mar 20, 2019

This course is very demanding. So, please listen to the lectures carefully.

创建者 Louis Hulot

•Mar 19, 2019

Fine introduction to DL (keras and tensorflow) with a good mathematic explanation of what we do.

创建者 Hemanth Kapila

•Mar 19, 2019

Good Course. Very informative and the assignments teach a lot.

创建者 Yuanxin Wang

•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!

创建者 Bruno FIEVET

•Mar 17, 2019

Tough course. You are happy and proud when you get it to the end.

创建者 Vaibhav Ojha

•Mar 17, 2019

Excellent hands on exercises to learn the basics

创建者 Ashirwad

•Mar 16, 2019

Best Course available on Deep Learning.

创建者 Emilio Pomares

•Mar 14, 2019

Very good quality learning and challenging assignments, as expected from an advanced course.

创建者 Aswin Rajeev

•Mar 13, 2019

Very peripheral teaching. Unable to understand anything from the touch points being covered. Assignments are not at all matching with the course contents.