Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
Этот курс входит в специализацию ''Профессиональная сертификация 'IBM AI Engineering''
Introduction to Deep Learning & Neural Networks with Keras
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Приобретаемые навыки
Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras
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Курс 3 из 6 в программе
Гибкие сроки
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Промежуточный уровень
Прибл. 9 часов на выполнение
Предполагаемая нагрузка: 2 - 3 hours/week
Английский
Субтитры: Английский
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IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Программа курса: что вы изучите
2 ч. на завершение
Introduction to Neural Networks and Deep Learning
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.
2 ч. на завершение
4 видео ((всего 17 мин.)), 1 материал для самостоятельного изучения, 2 тестов
4 видео
Welcome2мин
Introduction to Deep Learning4мин
Neurons and Neural Networks3мин
Artificial Neural Networks5мин
1 материал для самостоятельного изучения
Syllabus10мин
1 практическое упражнение
Introduction to Neural Networks and Deep Learning30мин
1 ч. на завершение
Artificial Neural Networks
In this module, you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.
1 ч. на завершение
4 видео ((всего 22 мин.))
1 практическое упражнение
Artificial Neural Networks30мин
3 ч. на завершение
Keras and Deep Learning Libraries
In this module, you will learn about the diifferent deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.
3 ч. на завершение
3 видео ((всего 16 мин.))
3 видео
Regression Models with Keras6мин
Classification Models with Keras5мин
1 практическое упражнение
Keras and Deep Learning Libraries30мин
2 ч. на завершение
Deep Learning Models
In this module, you will learn about the difference between the shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.
2 ч. на завершение
4 видео ((всего 17 мин.))
4 видео
Convolutional Neural Networks8мин
Recurrent Neural Networks2мин
Autoencoders2мин
1 практическое упражнение
Deep Learning Models30мин
Рецензии
4.7
Лучшие отзывы о курсе INTRODUCTION TO DEEP LEARNING & NEURAL NETWORKS WITH KERAS
от партнера ABMar 15, 2020
Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.
от партнера ASMar 19, 2020
A good course. Could be better if it was explained how to select the optimal number of layers and nodes. This was not covered and explained anywhere. Overall it was good.
от партнера FBMar 24, 2020
It is a really good course.\n\nThe labs could have had more for us to do, much of the labs was already implemented.\n\nStill, great introduction to the proposed subjects.
от партнера MCFeb 19, 2020
Best suited for beginner in Deep learning with Keras. Good content, hands on experience with Jupiter notebook with IBM Developer tool. Worked Exercise code for CNN & RNN.
от партнера SUMar 10, 2020
try to add more case study problems and solve it on lectures so that we can understand how to start (initialize) the coding part when we receive any real world problem.
от партнера RSFeb 20, 2020
I took this course for understanding the TensorFlow properly. Now I am in the situation to understand all the frameworks. Thanks a lot for providing me this free course
от партнера BJOct 9, 2019
Good practical examples for ANN. It could be improved the theoretical part and compare better the architecture of the networks with the algorithms and code for Keras
от партнера ASDec 15, 2019
Queries are not getting resolved in the discussion forum. So, instructors should participate in the discussion forum to resolve such queries.
Профессиональная сертификация 'IBM AI Engineering': общие сведения
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
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Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
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Записавшись на курс, вы получите доступ ко всем курсам в сертификации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
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