If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
Dieser Kurs ist Teil der Spezialisierung DeepLearning.AI TensorFlow Developer Zertifikat über berufliche Qualifikation
von
Über diesen Kurs
593,998 kürzliche Aufrufe
Karriereergebnisse der Lernenden
25%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
28%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 1 von 4 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
Ca. 30 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Chinesisch (vereinfacht), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch, Japanisch
Was Sie lernen werden
Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network
Kompetenzen, die Sie erwerben
Computer VisionTensorflowMachine Learning
Karriereergebnisse der Lernenden
25%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
28%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 1 von 4 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
Ca. 30 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Chinesisch (vereinfacht), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch, Japanisch
von
deeplearning.ai
Founded by Andrew Ng, DeepLearning.AI is an education technology company that develops a global community of AI talent.
Lehrplan - Was Sie in diesem Kurs lernen werden
6 Stunden zum Abschließen
A New Programming Paradigm
Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In week 1 you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along.
6 Stunden zum Abschließen
4 Videos (Gesamt 16 min), 5 Lektüren, 3 Quiz
4 Videos
A primer in machine learning3m
The ‘Hello World’ of neural networks5m
Working through ‘Hello World’ in TensorFlow and Python3m
5 Lektüren
Before you begin: TensorFlow 2.0 and this course10m
From rules to data10m
Try it for yourself10m
Introduction to Google Colaboratory10m
Week 1 Resources10m
1 praktische Übung
Week 1 Quiz
7 Stunden zum Abschließen
Introduction to Computer Vision
Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code!
7 Stunden zum Abschließen
7 Videos (Gesamt 15 min), 6 Lektüren, 3 Quiz
7 Videos
An Introduction to computer vision2m
Writing code to load training data2m
Coding a Computer Vision Neural Network2m
Walk through a Notebook for computer vision3m
Using Callbacks to control training1m
Walk through a notebook with Callbacks1m
6 Lektüren
Exploring how to use data10m
The structure of Fashion MNIST data10m
See how it's done10m
Get hands-on with computer vision1h
See how to implement Callbacks10m
Week 2 Resources10m
1 praktische Übung
Week 2 Quiz
8 Stunden zum Abschließen
Enhancing Vision with Convolutional Neural Networks
Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here.
8 Stunden zum Abschließen
6 Videos (Gesamt 19 min), 6 Lektüren, 3 Quiz
6 Videos
What are convolutions and pooling?2m
Implementing convolutional layers1m
Implementing pooling layers4m
Improving the Fashion classifier with convolutions4m
Walking through convolutions3m
6 Lektüren
Coding convolutions and pooling layers10m
Learn more about convolutions10m
Getting hands-on, your first ConvNet10m
Try it for yourself1h
Experiment with filters and pools1h
Week 3 Resources10m
1 praktische Übung
Week 3 Quiz
9 Stunden zum Abschließen
Using Real-world Images
Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!
9 Stunden zum Abschließen
9 Videos (Gesamt 27 min), 10 Lektüren, 3 Quiz
9 Videos
Understanding ImageGenerator4m
Defining a ConvNet to use complex images2m
Training the ConvNet with fit_generator2m
Walking through developing a ConvNet2m
Walking through training the ConvNet with fit_generator3m
Adding automatic validation to test accuracy4m
Exploring the impact of compressing images3m
A conversation with Andrew1m
10 Lektüren
Explore an impactful, real-world solution10m
Designing the neural network10m
Train the ConvNet with ImageGenerator10m
Exploring the solution10m
Training the neural network10m
Experiment with the horse or human classifier1h
Get hands-on and use validation30m
Get Hands-on with compacted images30m
Week 4 Resources10m
Wrap up10m
1 praktische Übung
Week 4 Quiz
Bewertungen
Top-Bewertungen von INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING
von AS8. März 2019
Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?
von JC30. Dez. 2020
I just can say that it was an awesome course. The instructors as well as the contents were clear, easy to understand and everything with a focus on how to take the theory and apply it with TensorFlow.
von JJ27. Apr. 2019
Very well organized. Good speakers. Content is comprehensive for a Introductory Course. A little more explanation on Validation versus Testing and on some of the evaluation functions would be helpful.
von NI18. Okt. 2020
Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.
Über den DeepLearning.AI TensorFlow Developer Zertifikat über berufliche Qualifikation
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.
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