Introduction to TensorFlow in Python Data Camp

Overview

Context

Not long ago, cutting-edge computer vision algorithms couldn’t differentiate between images of cats and dogs. Today, a skilled data scientist equipped with nothing more than a laptop can classify tens of thousands of objects with greater accuracy than the human eye.

In this Introduction to TensorFlow in Python course offered by Data Camp, you will use TensorFlow 2.6 to develop, train, and make predictions with the models that have powered major advances in recommendation systems, image classification, and FinTech.

Use Linear Models to Make Predictions

You’ll discover how to use TensorFlow 2.6 to make predictions using linear regression models, and will test out your knowledge by predicting house prices in King County. This section of the course includes a view of loss functions and how you can reduce your resource use by training your linear model in batches.

Train Your Neural Network

In the second half of the course, you’ll use the same tools to make predictions using neural networks. You’ll practice training a network in TensorFlow by adding trainable variables and using your model and test features to predict target values.

Combine TensorFlow with the Keras API

Add Keras’ powerful API to your repertoire and learn to combine it with TensorFlow 2.6 to make predictions and evaluate models. By the end of this course, you’ll understand how to use the Estimators API to streamline model definition and to avoid errors.