Spezialisierung Deep Learning

Spezialisierung Deep Learning

Name: Deep Learning
Rating: 4.852238304743232 (134776 reviews)

Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!

Dozenten: Andrew Ng

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5-teilige Kursreihe

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4.9

(134,776 Bewertungen)

Stufe Mittel

Empfohlene Erfahrung

Flexibler Zeitplan

3 Monate, 10 Stunden pro Woche

In Ihrem eigenen Lerntempo lernen

Auf einen Abschluss hinarbeiten

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5-teilige Kursreihe

Befassen Sie sich eingehend mit einem Thema

4.9

(134,776 Bewertungen)

Stufe Mittel

Empfohlene Erfahrung

Flexibler Zeitplan

3 Monate, 10 Stunden pro Woche

In Ihrem eigenen Lerntempo lernen

Auf einen Abschluss hinarbeiten

Mehr erfahren

Was Sie lernen werden

Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Kompetenzen, die Sie erwerben

Kategorie: Recurrent Neural Network
Kategorie: Tensorflow
Kategorie: Convolutional Neural Network
Kategorie: Artificial Neural Network
Kategorie: Transformers

Wichtige Details

Zertifikat zur Vorlage

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Spezialisierung - 5 Kursreihen

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Praktisches Lernprojekt

By the end you’ll be able to:

• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications

• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

Neural Networks and Deep Learning

KURS 124 Stunden4.9 (122,100 Bewertungen)

Was Sie lernen werden

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Kompetenzen, die Sie erwerben

Kategorie: Tensorflow

Kategorie: Deep Learning

Kategorie: hyperparameter tuning

Kategorie: Mathematical Optimization

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

KURS 223 Stunden4.9 (63,169 Bewertungen)

Was Sie lernen werden

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Kompetenzen, die Sie erwerben

Kategorie: Gated Recurrent Unit (GRU)

Kategorie: Recurrent Neural Network

Kategorie: Natural Language Processing

Kategorie: Long Short Term Memory (LSTM)

Kategorie: Attention Models

Structuring Machine Learning Projects

KURS 36 Stunden4.8 (49,903 Bewertungen)

Was Sie lernen werden

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Kompetenzen, die Sie erwerben

Kategorie: Artificial Neural Network

Kategorie: Backpropagation

Kategorie: Python Programming

Kategorie: Deep Learning

Kategorie: Neural Network Architecture

Convolutional Neural Networks

KURS 435 Stunden4.9 (42,317 Bewertungen)

Was Sie lernen werden

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Kompetenzen, die Sie erwerben

Kategorie: Decision-Making

Kategorie: Machine Learning

Kategorie: Deep Learning

Kategorie: Inductive Transfer

Kategorie: Multi-Task Learning

Sequence Models

KURS 537 Stunden4.8 (30,411 Bewertungen)

Was Sie lernen werden

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.

By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.

Kompetenzen, die Sie erwerben

Kategorie: Facial Recognition System

Kategorie: Tensorflow

Kategorie: Convolutional Neural Network

Kategorie: Deep Learning

Kategorie: Object Detection and Segmentation

Dozenten

TOP-LEHRKRAFT

Andrew Ng

DeepLearning.AI

45 Kurse7.830.962 Lernende

TOP-LEHRKRAFT

Younes Bensouda Mourri

DeepLearning.AI

23 Kurse1.549.451 Lernende

von

DeepLearning.AI

Auf einen Abschluss hinarbeiten

Wenn Sie dieses Spezialisierungabschließen, können Sie sich Ihr Wissen möglicherweise anrechnen lassen, wenn Sie zu einem der folgenden Online-Studiengänge zugelassen werden und sich dort einschreiben.¹

Anrechnung für Abschluss möglich

Dieser Spezialisierung ist ACE®-empfohlen. Teilnehmende US-amerikanischen Colleges und Universitäten vergeben Credits dafür. Hinweis: Die Entscheidung bezüglich spezifischer Credit-Empfehlungen liegt bei den einzelnen Institutionen.

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Warum entscheiden sich Menschen für Coursera für ihre Karriere?

Felipe M.

Lernender seit 2018

„Es ist eine großartige Erfahrung, in meinem eigenen Tempo zu lernen. Ich kann lernen, wenn ich Zeit und Nerven dazu habe.“

Jennifer J.

Lernender seit 2020

„Bei einem spannenden neuen Projekt konnte ich die neuen Kenntnisse und Kompetenzen aus den Kursen direkt bei der Arbeit anwenden.“

Larry W.

Lernender seit 2021

„Wenn mir Kurse zu Themen fehlen, die meine Universität nicht anbietet, ist Coursera mit die beste Alternative.“

Chaitanya A.

„Man lernt nicht nur, um bei der Arbeit besser zu werden. Es geht noch um viel mehr. Bei Coursera kann ich ohne Grenzen lernen.“

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Häufig gestellte Fragen

Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making. Neural networks with various (deep) layers enable learning through performing tasks repeatedly and tweaking them a little to improve the outcome.

Over the last few years, the availability of computing power and the amount of data being generated have led to an increase in deep learning capabilities. Today, deep learning engineers are highly sought after, and deep learning has become one of the most in-demand technical skills as it provides you with the toolbox to build robust AI systems that just weren’t possible a few years ago. Mastering deep learning opens up numerous career opportunities.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

By the end of the Deep Learning Specialization, you will be able to:

1. Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications. 2. Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow 3. Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning 4. Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data 5. Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

Expected:

Learners should have intermediate Python experience (e.g., basic programming skills, understanding of for loops, if/else statements, data structures such as lists and dictionaries).

Recommended:

Learners should have a basic knowledge of linear algebra (matrix-vector operations and notation).
Learners should have an understanding of machine learning concepts (how to represent data, what an ML model does, etc.)

The Deep Learning Specialization has been created by Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri.

Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform.

Kian Katanforoosh is the co-founder and CEO of Workera and a lecturer in the Computer Science department at Stanford University. Workera allows data scientists, machine learning engineers, and software engineers to assess their skills against industry standards and receive a personalized learning path. Kian is also the recipient of Stanford’s Walter J. Gores award (Stanford’s highest teaching award) and the Centennial Award for Excellence in teaching.

Younes Bensouda Mourri completed his Bachelor's in Applied Mathematics and Computer Science and Master's in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford - Applied Machine Learning, Deep Learning, and Teaching AI - and taught two of them for a few years.

All existing assignments and autograders have been refactored and updated to TensorFlow 2 across Courses 1, 2, 4, and 5.
Three new network architectures are presented with new lectures and programming assignments:

Course 4 includes MobileNet (transfer learning) and U-Net (semantic segmentation).
Course 5, once updated, will include Transformers (Network Architecture, Named Entity Recognition, Question Answering).

For a detailed list of changes, please check out the DLS Changelog.

• Your certificates will carry over for any courses you’ve already completed.

• If your subscription is currently active, you can access the updated labs and submit assignments without paying for the month again.

• If you go to the Specialization, you will see the original version of the lecture videos and assignments. You can complete the original version if so desired (this is not recommended).

• If you would like to update to the new material, reset your deadlines. If you’re in the middle of a course, you will lose your notebook work when you reset your deadlines. Please save your work by downloading your existing notebooks before switching to the new version.

• If you do not see the option to reset deadlines, contact Coursera via the Learner Help Center.

Those planning to attend a degree program can utilize ACE®️ recommendations, the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 10 college credits for completing the Deep Learning Specialization. This aims to help open up additional pathways to learners who are interested in higher education, and prepare them for entry-level jobs.

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credly badge, which contains the ACE®️ credit recommendation. Once claimed, they will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed.

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.

Weitere Fragen

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Spezialisierung Deep Learning

Was Sie lernen werden

Kompetenzen, die Sie erwerben

Wichtige Details

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

Erweitern Sie Ihre Fachkenntnisse.

Erwerben Sie ein Karrierezertifikat.