Über dieses Spezialisierung

746,929 kürzliche Aufrufe

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.

Karriereergebnisse der Lernenden
41%
Ich nahm nach Abschluss dieses Spezialisierung einen neuen Beruf auf.
14%
Ich erhielt eine Gehaltserhöhung oder Beförderung.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
Kurse, die komplett online stattfinden
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexibler Zeitplan
Festlegen und Einhalten flexibler Termine.
Stufe „Mittel“
Ca. 4 Monate zum Abschließen
Empfohlen werden 5 Stunden/Woche
Englisch
Untertitel: Englisch, Chinesisch (traditionell), Arabischer Raum, Französisch, Ukrainisch, Chinesisch (vereinfacht), Portugiesisch (Brasilien), Vietnamesisch, Koreanisch, Türkisch, Spanisch, Japanisch...
Karriereergebnisse der Lernenden
41%
Ich nahm nach Abschluss dieses Spezialisierung einen neuen Beruf auf.
14%
Ich erhielt eine Gehaltserhöhung oder Beförderung.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
Kurse, die komplett online stattfinden
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexibler Zeitplan
Festlegen und Einhalten flexibler Termine.
Stufe „Mittel“
Ca. 4 Monate zum Abschließen
Empfohlen werden 5 Stunden/Woche
Englisch
Untertitel: Englisch, Chinesisch (traditionell), Arabischer Raum, Französisch, Ukrainisch, Chinesisch (vereinfacht), Portugiesisch (Brasilien), Vietnamesisch, Koreanisch, Türkisch, Spanisch, Japanisch...

Es gibt 5 Kurse in dieser Spezialisierung

Kurs1

Kurs 1

Neuronale Netzwerke und Deep Learning

4.9
Sterne
86,154 Bewertungen
17,049 Bewertungen
Kurs2

Kurs 2

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

4.9
Sterne
50,473 Bewertungen
5,684 Bewertungen
Kurs3

Kurs 3

Structuring Machine Learning Projects

4.8
Sterne
40,655 Bewertungen
4,488 Bewertungen
Kurs4

Kurs 4

Convolutional Neural Networks

4.9
Sterne
33,285 Bewertungen
4,228 Bewertungen

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Häufig gestellte Fragen

  • 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.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • Expected:

    Programming experience. The course is taught in Python. We assume you have basic programming skills (understanding of for loops, if/else statements, data structures such as lists and dictionaries).

    Recommended:

    - Mathematics: basic linear algebra (matrix vector operations and notation) will help.

    - Machine Learning: a basic knowledge of machine learning (how do we represent data, what does a machine learning model do) will help. If you have taken Andrew Ng's Machine Learning course on Coursera, you're good of course!

  • No, these courses have sessions that start every few weeks. Once you enroll in a Specialization, you can take the courses at your own pace and even switch sessions if you fall behind. Please visit the Learner Help Center if you have any more questions about enrollment and sessions: https://learner.coursera.help/hc/en-us/articles/209818613

  • To request a receipt: In your Coursera account, open your My Purchases page. Find the course or Specialization you want a receipt for, and click "Email Receipt." The receipt will be sent within 24 hours. More instructions on requesting a receipt are here: https://learner.coursera.help/hc/en-us/articles/208280236

  • Please go to https://www.coursera.org/enterprise for more information, to contact Coursera, and to pick a plan. For each plan, you decide the number of courses each person can take and hand-pick the collection of courses they can choose from.

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