This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns.
von
Google CloudÜber diesen Kurs
42.095 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Ca. 19 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
Was Sie lernen werden
Use the Keras Sequential and Functional APIs for simple and advanced model creation
Design and build a TensorFlow 2.x input data pipeline
Use the tf.data library to manipulate data and large datasets
Train, deploy, and productionalize ML models at scale with Cloud AI Platform
Kompetenzen, die Sie erwerben
- Machine Learning
- Python Programming
- Build Input Data Pipeline
- Tensorflow
- keras
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Ca. 19 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
von
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Lehrplan - Was Sie in diesem Kurs lernen werden
3 Minuten zum Abschließen
Introduction to course
This course is an introduction to TensorFlow 2.x, which incorporates the ease of use of Keras for building machine learning models. This course covers designing and building a TensorFlow 2.x input data pipeline, building machine learning models with TensorFlow 2.x and Keras, improving the accuracy of machine learning models, and writing machine learning models for scaled use.
3 Minuten zum Abschließen
1 Video (Gesamt 3 min)
4 Stunden zum Abschließen
Introduction to TensorFlow
We will introduce you to the new paradigm of TensorFlow 2.x. You will learn about the the TensorFlow API hierarchy and will get to know the main components of TensorFlow, tensors and variables, through hands-on exercises.
4 Stunden zum Abschließen
6 Videos (Gesamt 27 min), 1 Lektüre, 5 Quiz
8 Stunden zum Abschließen
Design and Build a TensorFlow Input Data Pipeline
We will introduce you to working with datasets and feature columns. You will get hands-on practice loading csv data, numPy arrays with tf.data.Dataset, text data, and load images using tf.data.Dataset. You will also get hands on practice creating numeric, categorical, bucketized, and hashed feature columns.
8 Stunden zum Abschließen
10 Videos (Gesamt 25 min), 1 Lektüre, 9 Quiz
4 Stunden zum Abschließen
Training neural networks with Tensorflow 2 and the Keras Sequential API
In this module you will get introduced to writing TensorFlow models using the Keras Sequential API. But, before you jump right into writing the model, we will talk about activation functions, loss and optimization. You will then be introduced to the Keras Sequential API to learn how you can create deep learning models with it. You will also learn how to deploy the model for prediction in the cloud.
4 Stunden zum Abschließen
5 Videos (Gesamt 23 min), 1 Lektüre, 5 Quiz
3 Stunden zum Abschließen
Training neural networks with Tensorflow 2 and the Keras Functional API
The Sequential model API is great for developing deep learning models in most situations, but it also has some limitations. One example is that it is not straightforward to define models that may have multiple different input sources, produce multiple output destinations or models that re-use layers. The Keras Functional API is a way to create models that are more flexible than the tf.keras.Sequential API. The functional API can handle models with non-linear topology, models with shared layers, and models with multiple inputs or outputs. The Keras Functional API provides a more flexible way for defining models. It specifically allows you to define multiple input or output models as well as models that share layers. More than that, it allows you to define ad hoc acyclic network graphs. The main idea that a deep learning model is usually a directed acyclic graph (DAG) of layers. So the Functional API is a way to build graphs of layers. Additionally, we will also talk about how regularization can help with model performance.
3 Stunden zum Abschließen
6 Videos (Gesamt 29 min), 1 Lektüre, 4 Quiz
1 Stunde zum Abschließen
Summary
Here we summarize the TensorFlow topics we covered so far in this course. We'll revisit core TensorFlow code, the tf.data API, Keras Sequential and Functional APIs and end with scaling your machine learning models with Cloud AI Platform.
1 Stunde zum Abschließen
1 Video (Gesamt 8 min), 2 Lektüren, 1 Quiz
Bewertungen
Top-Bewertungen von VORSTELLUNG VON TENSORFLOW
von VC17. Mai 2020
I feel this course very valuable because it taught how to create an automated service in cloud with very huge data and working with distributed systems in production environment with minimal time.
von AJ14. Nov. 2020
Excellent 'Introduction' to TensorFlow 2.0 (HINT: 'Introduction' does not mean 'Easy'). Evan Jones is at his best giving rapid intuitive explanations of advanced topics in deep neural networks.
von LL30. Aug. 2019
The tools and methods presented were great. The instructors were also fantastic. However the coding exercises were lacking in guidance even though the complete solution is given in the video.
von DW16. Okt. 2018
pretty good. some of the code in the last lab could be better explained. also please debug the cloud shell, as it does not always show the "web preview" button ;) otherwise, good job!
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