Introduction to TensorFlow in Python

Overview

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Get an Introduction to TensorFlow

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 course, 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.

Syllabus

Introduction to TensorFlow

Before you can build advanced models in TensorFlow 2, you will first need to understand the basics. In this chapter, you’ll learn how to define constants and variables, perform tensor addition and multiplication, and compute derivatives. Knowledge of linear algebra will be helpful, but not necessary.

Linear models

In this chapter, you will learn how to build, solve, and make predictions with models in TensorFlow 2. You will focus on a simple class of models – the linear regression model – and will try to predict housing prices. By the end of the chapter, you will know how to load and manipulate data, construct loss functions, perform minimization, make predictions, and reduce resource use with batch training.

Neural Networks

The previous chapters taught you how to build models in TensorFlow 2. In this chapter, you will apply those same tools to build, train, and make predictions with neural networks. You will learn how to define dense layers, apply activation functions, select an optimizer, and apply regularization to reduce overfitting. You will take advantage of TensorFlow's flexibility by using both low-level linear algebra and high-level Keras API operations to define and train models.

High Level APIs

In the final chapter, you'll use high-level APIs in TensorFlow 2 to train a sign language letter classifier. You will use both the sequential and functional Keras APIs to train, validate, make predictions with, and evaluate models. You will also learn how to use the Estimators API to streamline the model definition and training process, and to avoid errors.