Introduction to TensorFlow
|
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. We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.
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. We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.
Table of Contents
- Introduction to course
3mins - Introduction to TensorFlow
26mins - Design and Build a TensorFlow Input Data Pipeline
26mins - Training neural networks with Tensorflow 2 and the Keras Sequential API
26mins - Training neural networks with Tensorflow 2 and Keras Functional API
29mins - Summary
8mins - Course Resource
0mins
User Reviews
Be the first to review “Introduction to TensorFlow”
Related Products
Intro to Language Models in Python
Build the basic language models in Python.
Machine Learning: Perceptrons
Level up your machine learning skills by learning how to build perceptrons: the foundations of neural networks.
Data Scientist: Machine Learning Specialist
Machine Learning Data Scientists solve problems at scale, make predictions, find patterns, and more! They use Python, SQL, and algorithms.
Data Scientist: Natural Language Processing Specialist
NLP Data Scientists find meaning in language, analyze text and speech, and create chatbots. They use Python, SQL, & NLP to answer questions.
Intro to PyTorch and Neural Networks
Learn how to use PyTorch to build, train, and test artificial neural networks in this course.
Machine Learning/AI Engineer
Machine Learning/AI Engineers build end-to-end ML applications and power many of the apps we use every day. They work in Python, Git, & ML.
There are no reviews yet.