# Introduction to TensorFlow in Python

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

Start Course for Free4 Hours15 Videos51 Exercises38,714 Learners4300 XPDeep Learning in Python TrackMachine Learning Scientist with Python Track

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## Course Description

## 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.- 1
### Introduction to TensorFlow

**Free**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.

Constants and variables50 xpDefining data as constants100 xpDefining variables100 xpBasic operations50 xpPerforming element-wise multiplication100 xpMaking predictions with matrix multiplication100 xpSumming over tensor dimensions50 xpAdvanced operations50 xpReshaping tensors100 xpOptimizing with gradients100 xpWorking with image data100 xp - 2
### 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.

Input data50 xpLoad data using pandas100 xpSetting the data type100 xpLoss functions50 xpLoss functions in TensorFlow100 xpModifying the loss function100 xpLinear regression50 xpSet up a linear regression100 xpTrain a linear model100 xpMultiple linear regression100 xpBatch training50 xpPreparing to batch train100 xpTraining a linear model in batches100 xp - 3
### 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.

Dense layers50 xpThe linear algebra of dense layers100 xpThe low-level approach with multiple examples100 xpUsing the dense layer operation100 xpActivation functions50 xpBinary classification problems100 xpMulticlass classification problems100 xpOptimizers50 xpThe dangers of local minima100 xpAvoiding local minima100 xpTraining a network in TensorFlow50 xpInitialization in TensorFlow100 xpDefining the model and loss function100 xpTraining neural networks with TensorFlow100 xp - 4
### 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.

Defining neural networks with Keras50 xpThe sequential model in Keras100 xpCompiling a sequential model100 xpDefining a multiple input model100 xpTraining and validation with Keras50 xpTraining with Keras100 xpMetrics and validation with Keras100 xpOverfitting detection100 xpEvaluating models100 xpTraining models with the Estimators API50 xpPreparing to train with Estimators100 xpDefining Estimators100 xpCongratulations!50 xp

Collaborators

Prerequisites

Supervised Learning with scikit-learn#### Isaiah Hull

Economist

Isaiah Hull is a visiting associate professor of finance at BI Norwegian Business School and the author of Machine Learning for Economics and Finance in TensorFlow 2. He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, and quantum computing.

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