Neuronale Netze und Deep Learning from Coursera

Wenn auch Sie topaktuelle KI für sich nutzen möchten, sind Sie mit diesem Kurs auf dem richtigen Weg. Deep Learning-Pioniere sind vielgefragt und wenn Sie Deep Learning einmal gemeistert haben, stehen Ihnen zahlreiche Karrieremöglichkeiten offen. Deep Learning ist eine neue „Superkraft“, mit der Sie KI-Systeme entwickeln können, die so vor ein paar Jahren gar nicht möglich gewesen wären.

Mit diesem Kurs eignen Sie sich die grundlegenden Kenntnisse zu Deep Learning an. Am Ende des Kurses werden Sie die folgenden Fähigkeiten erlangt haben:

– Verständnis der wesentlichen Techniktrends, die Deep Learning vorantreiben

– Erstellen, Trainieren und Anwenden lückenloser, tiefer neuronaler Netze

– Wissen, wie Sie effiziente (vektorisierte) neuronale Netze implementieren

– Verständnis der wichtigsten Parameter in der Architektur eines neuronalen Netzes

In diesem Kurs erfahren Sie zudem, wie Deep Learning eigentlich funktioniert, da das Konzept hier nicht nur flüchtig oder oberflächlich beschrieben wird Nach Abschluss des Kurses werden Sie in der Lage sein, Deep Learning für Ihre eigenen Anwendungen zu nutzen. Wenn Sie eine berufliche Laufbahn im Bereich KI anstreben, werden Sie nach diesem Kurs zudem grundlegende Fragen in einem Bewerbungsgespräch beantworten können.

Dies ist der erste Kurs der Deep Learning-Spezialisierung

What's inside

Syllabus

Neuronale Netze und Deep Learning

Seien Sie in der Lage, die wichtigsten Trends zu erklären, die Deep Learning vorantreiben, und verstehen Sie, wie diese heutzutage angewendet werden.

Grundlagen zu neuronalen Netzen

Lernen Sie, wie man ein maschinelles Lernproblem mit dem Denkmuster eines neuronalen Netzes erstellt. Erlernen Sie die Verwendung von Vektorisierung, um Ihre Modelle zu beschleunigen.

Flache neuronale Netze

Lernen Sie, wie Sie mithilfe von Forward Propagation und Backpropagation ein neuronales Netz mit einer verborgenen Schicht erstellen.

Tiefe neuronale Netze

Lernen Sie die wesentlichen Berechnungen, die dem Deep Learning zugrunde liegen, verstehen, verwenden Sie sie, um tiefe neuronale Netze zu erstellen, und wenden Sie sie auf die Computervision an.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

- Develops skills that are essential starting points in deep learning

- Offers opportunities to apply deep learning to real-life situations

- Provides a strong foundation in deep learning concepts

- Is ideal for beginners who want to learn the fundamentals of deep learning

- Course content is relevant to real-world applications

- Course is created by a team of experts with extensive experience in deep learning

Reviews summary

Expertly clarified deep learning

In this outstanding 5-star rated course, you will obtain a firm grasp of Deep Learning fundamentals. The instructor's exceptional clarity in explaining concepts and providing thorough examples makes this course highly recommended.

Career center

Learners who complete Neuronale Netze und Deep Learning will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Computer Vision Engineers design and develop computer vision systems that can interpret and understand images and videos. This course can help you gain the skills necessary to build and train deep neural networks, which are essential for developing and deploying AI-powered computer vision solutions. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in computer vision. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Machine Learning Engineer

Machine Learning Engineers design and implement machine learning algorithms to solve real-world problems. This course can help you gain the skills necessary to build and train deep neural networks, which are essential for developing and deploying AI-powered machine learning solutions. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Data Scientist

Data Scientists analyze and interpret large amounts of data using statistical algorithms and machine learning techniques to extract meaningful insights. This course can help you gain the skills necessary to build and train deep neural networks, which are essential for developing and deploying AI-powered data science solutions. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Natural Language Processing Engineer

Natural Language Processing Engineers design and develop natural language processing systems that can understand and generate human language. This course can help you gain the skills necessary to build and train deep neural networks, which are essential for developing and deploying AI-powered natural language processing solutions. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in natural language processing. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Speech Recognition Engineer

Speech Recognition Engineers design and develop speech recognition systems that can recognize and understand human speech. This course can help you gain the skills necessary to build and train deep neural networks, which are essential for developing and deploying AI-powered speech recognition solutions. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in speech recognition. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in quantitative analysis. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in quantitative analysis. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve complex business problems. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in operations research. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in operations research. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in software development. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Bioinformatician

Bioinformaticians use computational tools to analyze biological data. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in bioinformatics. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in bioinformatics. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Neuroscientist

Neuroscientists study the nervous system and brain. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in neuroscience. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in neuroscience. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Cognitive Scientist

Cognitive Scientists study the mind and cognition. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in cognitive science. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in cognitive science. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Data Analyst

Data Analysts analyze data to extract meaningful insights. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in data analysis. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in data analysis. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make better decisions. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in business intelligence. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in business intelligence. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Robotics Engineer

Robotics Engineers design and develop robots. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in robotics. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in robotics. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. This course can help you gain the skills necessary to build and train deep neural networks, which are becoming increasingly important in financial analysis. The course covers the fundamentals of deep learning, including the different types of neural networks and their applications in financial analysis. You will also learn about the challenges and best practices of deep learning, as well as the ethical implications of using AI.

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