This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.
Этот курс входит в специализацию ''Специализация Глубокое обучение'
от партнера
Специализация Глубокое обучениеdeeplearning.aiОб этом курсе
Недавно просмотрено: 1,609,096
Карьерные результаты учащихся
41%
начал новую карьеру, пройдя эти курсы
37%
получил значимые преимущества в карьере благодаря этому курсу
12%
стал больше зарабатывать или получил повышение
Сертификат, ссылками на который можно делиться с другими людьми
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100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 2 из 5 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Начальный уровень
Прибл. 18 часов на выполнение
Английский
Субтитры: Китайский (традиционное письмо), Китайский (упрощенное письмо), Португальский (бразильский), Корейский, Турецкий, Английский, Испанский
Приобретаемые навыки
HyperparameterTensorflowHyperparameter OptimizationDeep Learning
Карьерные результаты учащихся
41%
начал новую карьеру, пройдя эти курсы
37%
получил значимые преимущества в карьере благодаря этому курсу
12%
стал больше зарабатывать или получил повышение
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 2 из 5 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Начальный уровень
Прибл. 18 часов на выполнение
Английский
Субтитры: Китайский (традиционное письмо), Китайский (упрощенное письмо), Португальский (бразильский), Корейский, Турецкий, Английский, Испанский
от партнера
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
Программа курса: что вы изучите
8 ч. на завершение
Practical aspects of Deep Learning
8 ч. на завершение
15 видео ((всего 131 мин.)), 3 материалов для самостоятельного изучения, 4 тестов
15 видео
Bias / Variance8мин
Basic Recipe for Machine Learning6мин
Regularization9мин
Why regularization reduces overfitting?7мин
Dropout Regularization9мин
Understanding Dropout7мин
Other regularization methods8мин
Normalizing inputs5мин
Vanishing / Exploding gradients6мин
Weight Initialization for Deep Networks6мин
Numerical approximation of gradients6мин
Gradient checking6мин
Gradient Checking Implementation Notes5мин
Yoshua Bengio interview25мин
3 материала для самостоятельного изучения
Clarification about Upcoming Regularization Video1мин
Clarification about Upcoming Understanding dropout Video1мин
Clarification about Upcoming Normalizing Inputs Video1мин
1 практическое упражнение
Practical aspects of deep learning30мин
5 ч. на завершение
Optimization algorithms
5 ч. на завершение
11 видео ((всего 92 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
11 видео
Understanding mini-batch gradient descent11мин
Exponentially weighted averages5мин
Understanding exponentially weighted averages9мин
Bias correction in exponentially weighted averages4мин
Gradient descent with momentum9мин
RMSprop7мин
Adam optimization algorithm7мин
Learning rate decay6мин
The problem of local optima5мин
Yuanqing Lin interview13мин
2 материала для самостоятельного изучения
Clarification about Upcoming Adam Optimization Video1мин
Clarification about Learning Rate Decay Video1мин
1 практическое упражнение
Optimization algorithms30мин
5 ч. на завершение
Hyperparameter tuning, Batch Normalization and Programming Frameworks
5 ч. на завершение
11 видео ((всего 104 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
11 видео
Tuning process7мин
Using an appropriate scale to pick hyperparameters8мин
Hyperparameters tuning in practice: Pandas vs. Caviar6мин
Normalizing activations in a network8мин
Fitting Batch Norm into a neural network12мин
Why does Batch Norm work?11мин
Batch Norm at test time5мин
Softmax Regression11мин
Training a softmax classifier10мин
Deep learning frameworks4мин
TensorFlow16мин
2 материала для самостоятельного изучения
Clarifications about Upcoming Softmax Video1мин
Note about TensorFlow 1 and TensorFlow 210мин
1 практическое упражнение
Hyperparameter tuning, Batch Normalization, Programming Frameworks30мин
Рецензии
Лучшие отзывы о курсе IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION
от партнера A22 окт. 2017 г.
Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course
от партнера AS18 апр. 2020 г.
Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course
от партнера AM8 окт. 2019 г.
I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation
от партнера XG30 окт. 2017 г.
Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.
Специализация Глубокое обучение: общие сведения
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
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