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.

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Neuronale Netze und Deep Learning with these activities:

Review relevant research papers

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Review key research papers in the field of deep learning to build a strong foundation before starting the course.

Browse courses on Neural Networks

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Identify relevant research papers through reputable sources such as Google Scholar or academic databases.
Read and summarize the key findings and methodologies of the selected papers.

Follow online tutorials on neural networks

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Reinforce your understanding of neural networks by following guided tutorials provided by experienced practitioners.

Browse courses on Neural Networks

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Search for reputable online tutorials or courses on neural networks.
Complete the tutorials, following the instructions and practicing the exercises.

Build a simple neural network using TensorFlow

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Apply your knowledge by creating a basic neural network model using TensorFlow to gain hands-on experience.

Browse courses on TensorFlow

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Choose a dataset and define the problem you want to solve.
Design and implement the neural network architecture using TensorFlow.
Train and evaluate the neural network.

Three other activities

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Solve coding exercises on LeetCode or similar platforms

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Sharpen your coding skills by practicing solving coding exercises related to deep learning concepts.

Browse courses on Coding

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Register on platforms like LeetCode or HackerRank.
Solve coding exercises tagged with deep learning or related topics.

Join a study group or online forum

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Engage with peers to discuss course concepts, share insights, and learn from others' perspectives.

Browse courses on Deep Learning

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Search for online forums or study groups dedicated to deep learning.
Actively participate in discussions, ask questions, and share your knowledge.

Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

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Gain a comprehensive understanding of deep learning theory and foundations by reading this authoritative book.

View Deep Learning. Das umfassende Handbuch on Amazon

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Acquire a copy of 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Read and thoroughly study the book's chapters.

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.

See salaries and explore the career path for Computer Vision Engineer

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Natural Language Processing Engineer

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.

See salaries and explore the career path for Speech Recognition Engineer

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Cognitive Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Financial Analyst