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.
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IBMОб этом курсе
Недавно просмотрено: 36 669
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100% онлайн
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Средний уровень
Прибл. 8 часов на выполнение
Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
Приобретаемые навыки
Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Средний уровень
Прибл. 8 часов на выполнение
Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
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IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
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
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.))
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.))
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.))
Рецензии
Лучшие отзывы о курсе INTRODUCTION TO DEEP LEARNING & NEURAL NETWORKS WITH KERAS
от партнера AB15 мар. 2020 г.
Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.
от партнера AM24 июня 2020 г.
Good course. It is a very direct approach. It is a basic introduction to keras. Doing the labs is recommended, and also previous knowledge about machine learning is encouraged.
от партнера MC19 февр. 2020 г.
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 AS19 мар. 2020 г.
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.
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