## 加入課程

Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習，請直接註冊專項課程，或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時，您將自動訂閱整個專項課程。您可以只完成一門課程，您可以隨時暫停學習或結束訂閱。訪問您的學生面板，跟踪您的課程註冊情況和進度。

## 獲得證書

### Introduction to Deep Learning

4.6
872 個評分
195 個審閱
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....

4.7
529 個評分
122 個審閱

### Bayesian Methods for Machine Learning

4.6
315 個評分
88 個審閱
People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods....

### Practical Reinforcement Learning

4.1
183 個評分
51 個審閱
Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems. - and, of course, teaching your neural network to play games --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits. Jump in. It's gonna be fun!...

## 講師

### Mikhail Hushchyn

Researcher at Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science

### Alexey Zobnin

Accosiate professor
HSE Faculty of Computer Science

### Alexey Artemov

Senior Lecturer
HSE Faculty of Computer Science

### Sergey Yudin

Analyst-developer
Yandex

### Alexander Guschin

Visiting lecturer at HSE, Lecturer at MIPT
HSE Faculty of Computer Science

### Nikita Kazeev

Researcher
HSE Faculty of Computer Science

### Andrei Ustyuzhanin

Head of Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science

### Dmitry Ulyanov

Visiting lecturer
HSE Faculty of Computer Science

### Marios Michailidis

Research Data Scientist
H2O.ai

### Daniil Polykovskiy

Researcher
HSE Faculty of Computer Science

### Ekaterina Lobacheva

Senior Lecturer
HSE Faculty of Computer Science

### Andrei Zimovnov

Senior Lecturer
HSE Faculty of Computer Science

### Alexander Novikov

Researcher
HSE Faculty of Computer Science

### Dmitry Altukhov

Visiting lecturer
HSE Faculty of Computer Science

### Pavel Shvechikov

Researcher at HSE and Sberbank AI Lab
HSE Faculty of Computer Science

### Anton Konushin

Senior Lecturer
HSE Faculty of Computer Science

Yandex

### Mikhail Trofimov

Visiting lecturer
HSE Faculty of Computer Science

### Evgeny Sokolov

Senior Lecturer
HSE Faculty of Computer Science

### Alexander Panin

Lecturer
HSE Faculty of Computer Science

### Anna Potapenko

Researcher
HSE Faculty of Computer Science

## 關於 国立高等经济大学

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 communicamathematics, engineering, and more. Learn more on www.hse.ru...

## 常見問題

• 可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。

• 此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

• Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 8-10 months.

• As prerequisites we assume calculus and linear algebra (especially derivatives, matrices and operations with them), probability theory (random variables, distributions, moments), basic programming in python (functions, loops, numpy), basic machine learning (linear models, decision trees, boosting and random forests). Our intended audience are all people who are already familiar with basic machine learning and want to get a hand-on experience of research and development in the field of modern machine learning.

• We recommend taking the “Intro to Deep Learning” course first as most of the subsequent courses will build on its material. All other courses can be taken in any order.

• Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

• After completing 7 courses of the Specialization you will be able to:

Use modern deep neural networks for various machine learning problems with complex inputs;

Participate in data science competitions and use the most popular and effective machine learning tools;

Adopt the best practices of data exploration, preprocessing and feature engineering;

Perform Bayesian inference, understand Bayesian Neural Networks and Variational Autoencoders;

Use reinforcement learning methods to build agents for games and other environments;

Solve computer vision problems with a combination of deep models and classical computer vision algorithms;

Outline state-of-the-art techniques for natural language tasks, such as sentiment analysis, semantic slot filling, summarization, topics detection, and many others;

Build goal-oriented dialogue agents and train them to hold a human-like conversation;

Understand limitations of standard machine learning methods and design new algorithms for new tasks.