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Introduction to TensorFlow in Python

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

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4 Hours15 Videos51 Exercises37,237 Learners4300 XPDeep Learning TrackMachine Learning Scientist 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. 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.

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    Constants and variables
    50 xp
    Defining data as constants
    100 xp
    Defining variables
    100 xp
    Basic operations
    50 xp
    Performing element-wise multiplication
    100 xp
    Making predictions with matrix multiplication
    100 xp
    Summing over tensor dimensions
    50 xp
    Advanced operations
    50 xp
    Reshaping tensors
    100 xp
    Optimizing with gradients
    100 xp
    Working with image data
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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In the following tracks

Deep LearningMachine Learning Scientist

Collaborators

alexandrayarosh
Alex Yarosh
sara-billen
Sara Billen
mona-kay
Mona Khalil
Isaiah Hull Headshot

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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