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.
Этот курс входит в специализацию ''Специализация Machine Learning with TensorFlow on Google Cloud Platform'
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Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
Чему вы научитесь
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
Приобретаемые навыки
Machine LearningPython ProgrammingBuild Input Data PipelineTensorflowkeras
Карьерные результаты учащихся
25%
начал новую карьеру, пройдя эти курсы
31%
получил значимые преимущества в карьере благодаря этому курсу
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 3 из 5 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Средний уровень
Прибл. 19 часов на выполнение
Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
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Программа курса: что вы изучите
7 минут на завершение
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.
7 минут на завершение
2 видео ((всего 7 мин.))
3 ч. на завершение
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.
3 ч. на завершение
5 видео ((всего 22 мин.)), 1 материал для самостоятельного изучения, 5 тестов
8 ч. на завершение
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 ч. на завершение
10 видео ((всего 25 мин.)), 1 материал для самостоятельного изучения, 9 тестов
4 ч. на завершение
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 ч. на завершение
5 видео ((всего 23 мин.)), 1 материал для самостоятельного изучения, 5 тестов
3 ч. на завершение
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 ч. на завершение
6 видео ((всего 29 мин.)), 1 материал для самостоятельного изучения, 4 тестов
1 ч. на завершение
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 ч. на завершение
1 видео ((всего 8 мин.)), 2 материалов для самостоятельного изучения, 1 тест
Рецензии
Лучшие отзывы о курсе ВВЕДЕНИЕ В TENSORFLOW
от партнера AJ14 нояб. 2020 г.
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.
от партнера VC17 мая 2020 г.
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.
от партнера LL30 авг. 2019 г.
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.
от партнера DW16 окт. 2018 г.
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!
Специализация Machine Learning with TensorFlow on Google Cloud Platform: общие сведения
What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
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