Become a Deep Learning expert. Master the fundamentals of deep learning and break into AI.

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Специализация Глубокое обучениеdeeplearning.ai

Чему вы научитесь

Build & train deep neural networks, identify key architecture parameters, & implement efficient neural networks & deep learning to your applications

Train test sets & analyze variance for DL applications, use standard techniques & optimization algorithms, & build neural networks in TensorFlow

Build a CNN & apply it to detection & recognition tasks, use neural style transfer to generate art, & apply algorithms to image, video, & 2D/3D data

Build & train RNNs, work with NLP & Word Embeddings, & use HuggingFace tokenizers & transformer models to perform tasks like NER & Question Answering

Приобретаемые навыки

Artificial Neural NetworkConvolutional Neural NetworkTensorflowRecurrent Neural NetworkTransformersDeep LearningBackpropagationPython ProgrammingNeural Network ArchitectureMathematical Optimizationhyperparameter tuningInductive Transfer

Специализация: общие сведения

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Along the way, you will get career advice from deep learning experts from industry and academia.

Проект прикладного обучения

By the end of the program, you'll be able to

• Build and train deep neural networks, implement vectorized neural networks, identify key parameters in architecture, and apply deep learning to your applications

• Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning

• Build a CNN, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

• Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering

Карьерные результаты учащихся

38%

Начали новую карьеру, пройдя этот продукт (Специализация).

14%

Стали больше зарабатывать или получили повышение.

Сертификат, ссылками на который можно делиться с другими людьми

Получите сертификат по завершении

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкий график

Установите гибкие сроки сдачи заданий.

Средний уровень

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approximately 4 months to complete

Suggested pace of 5 hours/week

Английский

Субтитры: Английский, Китайский (традиционное письмо), Арабский, Французский, Украинский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Португальский (бразильский), Вьетнамский, Корейский, Немецкий, Русский, Турецкий, Испанский, Японский

Карьерные результаты учащихся

38%

Начали новую карьеру, пройдя этот продукт (Специализация).

14%

Стали больше зарабатывать или получили повышение.

Сертификат, ссылками на который можно делиться с другими людьми

Получите сертификат по завершении

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкий график

Установите гибкие сроки сдачи заданий.

Средний уровень

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approximately 4 months to complete

Suggested pace of 5 hours/week

Английский

Специализация включает несколько курсов: 5

Курс1

Курс 1

Нейронные сети и глубокое обучение

4.9

звезд

Оценки: 103,180

•

Рецензии: 20,639

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Курс2

Курс 2

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

4.9

звезд

Оценки: 57,409

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Рецензии: 6,599

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Курс3

Курс 3

Structuring Machine Learning Projects

4.8

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Оценки: 45,740

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Рецензии: 5,220

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Курс4

Курс 4

Convolutional Neural Networks

4.9

звезд

Оценки: 38,261

•

Рецензии: 5,020

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Преподаватели

от партнера

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

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Эта специализация не приравнивается к зачету в университетах, однако некоторые вузы принимают сертификаты на свое усмотрение. Дополнительную информацию уточняйте в своем деканате.
Получу ли я зачеты в университете за прохождение специализации?
Эта специализация не приравнивается к зачету в университетах, однако некоторые вузы принимают сертификаты на свое усмотрение. Дополнительную информацию уточняйте в своем деканате. Онлайн-дипломы и сертификаты Mastertrack™ от Coursera позволяют получить зачеты.
What is the Deep Learning Specialization about?
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing.
AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Along the way, you will get career advice from deep learning experts from industry and academia.
What will I be able to do after completing the Deep Learning Specialization?
By the end of the Deep Learning Specialization, you will be able to:
1. Build and train deep neural networks, implement vectorized neural networks, identify key parameters in architecture, and apply deep learning to your applications 2. Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow 3. Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning 4. Build a CNN, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data 5. Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering
What background knowledge is necessary for the Deep Learning Specialization?
Expected:
Learners should have intermediate Python experience (e.g., basic programming skills, understanding of for loops, if/else statements, data structures such as lists and dictionaries).
Recommended:
Learners should have a basic knowledge of linear algebra (matrix-vector operations and notation).
Learners should have an understanding of machine learning concepts (how to represent data, what an ML model does, etc.)
Who is the Deep Learning Specialization for?
The Deep Learning Specialization is for early-career software engineers or technical professionals looking to master fundamental concepts and gain practical machine learning and deep learning skills.
How long does it take to complete the Deep Learning Specialization?
The Deep Learning Specialization consists of five courses. At the rate of 5 hours a week, it typically takes 5 weeks to complete each course except course 3, which takes about 4 weeks.
Who is the Deep Learning Specialization by?
The Deep Learning Specialization has been created by Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri.
Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform.
Kian Katanforoosh is the co-founder and CEO of Workera and a lecturer in the Computer Science department at Stanford University. Workera allows data scientists, machine learning engineers, and software engineers to assess their skills against industry standards and receive a personalized learning path. Kian is also the recipient of Stanford’s Walter J. Gores award (Stanford’s highest teaching award) and the Centennial Award for Excellence in teaching.
Younes Bensouda Mourri completed his Bachelor's in Applied Mathematics and Computer Science and Master's in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford - Applied Machine Learning, Deep Learning, and Teaching AI - and taught two of them for a few years.
Is this a standalone course or a Specialization?
The Deep Learning Specialization is made up of 5 courses.
Do I need to take the courses in a specific order?
We recommend taking the courses in the prescribed order for a logical and thorough learning experience. Course 3 can also be taken as a standalone course.
Can I apply for financial aid?
Yes, Coursera provides financial aid to learners who cannot afford the fee.
Can I audit the Deep Learning Specialization?
You can audit the courses in the Deep Learning Specialization for free.
Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.
How do I get a receipt to get this reimbursed by my employer?
Go to your Coursera account.
Click on My Purchases and find the relevant course or Specialization.
Click Email Receipt and wait up to 24 hours to receive the receipt.
You can read more about it here.
I want to purchase this Specialization for my employees! How can I do that?
Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

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