课程信息
4.5
545 ratings
137 reviews
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....
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100% 在线课程

立即开始,按照自己的计划学习。
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可灵活调整截止日期

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Advanced Level

高级

Clock

建议:6 weeks of study, 6-10 hours/week

完成时间大约为36 小时
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English

字幕:English

您将获得的技能

Recurrent Neural NetworkTensorflowConvolutional Neural NetworkDeep Learning
Stacks
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Advanced Level

高级

Clock

建议:6 weeks of study, 6-10 hours/week

完成时间大约为36 小时
Comment Dots

English

字幕:English

教学大纲 - 您将从这门课程中学到什么

1

章节
Clock
完成时间为 5 小时

Introduction to optimization

Welcome to the "Introduction to Deep Learning" course! In the first week you'll learn about linear models and stochatic optimization methods. Linear models are basic building blocks for many deep architectures, and stochastic optimization is used to learn every model that we'll discuss in our course....
Reading
9 个视频(共 63 分钟), 2 个阅读材料, 3 个测验
Video9 个视频
Course intro6分钟
Linear regression9分钟
Linear classification10分钟
Gradient descent5分钟
Overfitting problem and model validation6分钟
Model regularization5分钟
Stochastic gradient descent5分钟
Gradient descent extensions9分钟
Reading2 个阅读材料
Welcome!5分钟
Hardware for the course10分钟
Quiz2 个练习
Linear models6分钟
Overfitting and regularization8分钟

2

章节
Clock
完成时间为 6 小时

Introduction to neural networks

This module is an introduction to the concept of a deep neural network. You'll begin with the linear model and finish with writing your very first deep network....
Reading
9 个视频(共 85 分钟), 3 个阅读材料, 4 个测验
Video9 个视频
Chain rule7分钟
Backpropagation9分钟
Efficient MLP implementation13分钟
Other matrix derivatives5分钟
What is TensorFlow10分钟
Our first model in TensorFlow10分钟
What Deep Learning is and is not8分钟
Deep learning as a language6分钟
Reading3 个阅读材料
Optional reading on matrix derivatives1分钟
TensorFlow reading1分钟
Keras reading1分钟
Quiz2 个练习
Multilayer perceptron10分钟
Matrix derivatives20分钟

3

章节
Clock
完成时间为 5 小时

Deep Learning for images

In this week you will learn about building blocks of deep learning for image input. You will learn how to build Convolutional Neural Network (CNN) architectures with these blocks and how to quickly solve a new task using so-called pre-trained models....
Reading
6 个视频(共 59 分钟), 3 个测验
Video6 个视频
Our first CNN architecture10分钟
Training tips and tricks for deep CNNs14分钟
Overview of modern CNN architectures8分钟
Learning new tasks with pre-trained CNNs5分钟
A glimpse of other Computer Vision tasks8分钟
Quiz1 个练习
Convolutions and pooling10分钟

4

章节
Clock
完成时间为 4 小时

Unsupervised representation learning

This week we're gonna dive into unsupervised parts of deep learning. You'll learn how to generate, morph and search images with deep learning....
Reading
9 个视频(共 81 分钟), 3 个测验
Video9 个视频
Autoencoders 1015分钟
Autoencoder applications9分钟
Autoencoder applications: image generation, data visualization & more7分钟
Natural language processing primer10分钟
Word embeddings13分钟
Generative models 1017分钟
Generative Adversarial Networks10分钟
Applications of adversarial approach11分钟
Quiz1 个练习
Word embeddings8分钟
4.5
Direction Signs

22%

完成这些课程后已开始新的职业生涯
Briefcase

83%

通过此课程获得实实在在的工作福利

热门审阅

创建者 YGJan 28th 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!

创建者 ASMar 26th 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.

讲师

Evgeny Sokolov

Senior Lecturer
HSE Faculty of Computer Science

Andrei Zimovnov

Senior Lecturer
HSE Faculty of Computer Science

Alexander Panin

Lecturer
HSE Faculty of Computer Science

Ekaterina Lobacheva

Senior Lecturer
HSE Faculty of Computer Science

Nikita Kazeev

Researcher
HSE Faculty of Computer Science

关于 National Research University Higher School of Economics

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more. Learn more on www.hse.ru...

关于 Advanced Machine Learning 专项课程

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings....
Advanced Machine Learning

常见问题

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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