20 Related Courses
for Pluralsight Tensorflow CoursesBuild a Machine Learning Workflow with Keras TensorFlow 20
This course provides an introduction to building a machine learning workflow with Keras and TensorFlow 2.0. It covers the use of sequential APIs, functional APIs, model subclassing, and custom layers to create complex models. View all
Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 24
This course provides an introduction to deep learning techniques with TensorFlow 2.4, focusing on the application of these methods to detect and mitigate anomalies in data such as time series. Participants will gain the skills to create deep-learning algorithms for data anomaly detection. View all
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Building Machine Learning Solutions with TensorFlowjs
This course teaches developers how to use TensorFlow.js to create, train, and deploy machine learning and deep learning models in JavaScript. Participants will gain the skills to power client-side and server-side applications with machine learning, enabling them to build powerful machine learning solutions. View all
Designing Data Pipelines with TensorFlow 20
This course explores the tf.data module, a unified interface for managing data pipelines in TensorFlow 2.0. Participants will learn how to use this module to simplify and streamline their data pipelines. View all
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Implementing Predictive Analytics with TensorFlow
This course provides an introduction to predictive analytics using TensorFlow, a popular open-source library for data science and machine learning. Participants will learn the fundamentals of supervised learning, recommendation, and reinforcement systems, enabling them to build powerful predictive models. View all
Implementing Multi-layer Neural Networks with TFLearn
This course provides an introduction to deep learning and TFLearn, allowing learners to quickly build and deploy multi-layer neural networks. With the help of Tensorflow and TFLearn, learners will gain the skills to create and implement their own neural networks. View all
Recommendation Systems with TensorFlow on Google Cloud
In this course, learners will use TensorFlow and Google Cloud to build a recommendation system. Through the use of classification models and embeddings, learners will create a machine learning pipeline to create a powerful recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series. View all
End-to-End Machine Learning with TensorFlow on GCP
This course provides an in-depth exploration of End-to-End Machine Learning with TensorFlow on Google Cloud Platform. Participants will learn to build an end-to-end model from data exploration to deploying an ML model and obtaining predictions. View all
Serverless Machine Learning with Tensorflow on Google Cloud Platform
This course provides an introduction to building, scaling, and operationalizing machine learning models with TensorFlow on Google Cloud Platform. Participants will learn how to use Cloud ML Engine to train and deploy models in a serverless environment. View all
Image Understanding with TensorFlow on GCP
This course provides an overview of strategies for building an image classifier using convolutional neural networks on Google Cloud Platform. Participants will learn how to improve accuracy with augmentation, feature extraction, and hyperparameter tuning, as well as how to address practical issues such as data scarcity. Through hands-on labs, participants will gain experience building and optimizing image classification models on public datasets. View all
Introduction to TensorFlow
This course provides an introduction to TensorFlow, a powerful tool for creating machine learning models. Students will learn how to use the TensorFlow libraries to solve numerical problems, debug common errors, and use the Estimator API to create, train, and evaluate ML models. Finally, they will learn how to deploy and productionalize ML models at scale with Cloud AI Platform. View all
Getting Started with Tensorflow 20
This course provides an introduction to TensorFlow 2.0, exploring its features and functionality for building and training neural networks. It covers the differences between TensorFlow 1.x and 2.0, as well as how the Keras high-level API and eager execution make TensorFlow 2.0 easy to use for complex models. View all
Deploying TensorFlow Models to AWS Azure and the GCP
This course provides an overview of how to deploy TensorFlow models to the cloud platforms of Azure, AWS, and GCP. Learn how to take your TensorFlow model and deploy it locally or to the cloud of your choice. View all
Recommendation Systems with TensorFlow on GCP
This course provides an introduction to building recommendation systems with TensorFlow on Google Cloud Platform. Students will learn to use classification models and embeddings to create a machine learning pipeline that functions as a recommendation engine. View all
Build Train and Deploy Your First Neural Network with TensorFlow
Explore the essentials of Build Train and Deploy Your First Neural Network with TensorFlow View all
Introduction to TensorFlow
Discover the fundamentals of Introduction to TensorFlow View all
Introduction to TensorFlow
This course introduces participants to TensorFlow 2.x and Keras, providing hands-on experience with building, training, and deploying machine learning models. Participants will learn about the TensorFlow 2.x API hierarchy, datasets, feature columns, and the Keras Sequential and Functional APIs. Labs offer practice with linear and logistic regression models, and Cloud AI Platform is used to productionalize models. View all
TensorFlow on Google Cloud
This course provides an introduction to using TensorFlow 2.x and Keras to design and build ML models, improve accuracy, and scale for production use. Participants will learn how to create an input data pipeline, build ML models, and write specialized ML models. View all
TensorFlow on Google Cloud
Explore the essentials of TensorFlow on Google Cloud View all
Build Train and Deploy Your First Neural Network with TensorFlow
This course will teach you the fundamentals of machine learning and neural networks so that you can quickly create, train, and deploy a neural network using TensorFlow. View all
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