Basic Image Classification with TensorFlow

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682 ratings
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101 reviews
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15,036 already enrolled
In this Guided Project, you will:

Create, train and evaluate a neural network in TensorFlow

Understand the basics of neural networks

Solve classification problems with neural networks

2 hours
Beginner
No download needed
Split-screen video
English
Desktop only

In this 2-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and use it to solve a basic image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that will be able to predict digits from hand-written images with a high degree of accuracy. You also will have learned the fundamentals of neural networks, TensorFlow, and Keras. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • Deep Learning

  • Artificial Neural Network

  • Machine Learning

  • Tensorflow

  • keras

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Encode the labels

  2. Understand neural networks

  3. Preprocess image examples

  4. Create a neural network model

  5. Train the model to fit the dataset

  6. Evaluate the model

  7. Visualize the predictions

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Instructor

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Frequently asked questions

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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

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