In this 90-min long project-based course you will learn how to use Tensorflow to construct neural network models. Specifically, we will design, execute, and evaluate a neural network model to help a retail company with their marketing campaign by classifying images of clothing items into 10 different categories. Throughout this course, you will learn how to use Tensorflow to build and analyze neural neural networks that can perform multi-label classification for applications in image recognition. You will also be able to identify and adapt the main components of neural networks as well as evaluate the performance of different models and implement measures to improve their accuracy. At the end of the project, you will be able to design and implement convolutional neural networks helping a retail store with their targeted ad campaign, and the models can be easily adapted for self-driving cars, computer-assisted medical diagnosis, etc.
Recommended experience
What you'll learn
Adapt the main components of neural networks: inputs, layers, weights, and activation functions according to the specific application.
Use TensorFlow and Keras to design, implement, and adapt convolutional neural networks for image recognition tasks.
Evaluate neural network models and measure their accuracy, modify the parameters of the model if needed to improve its accuracy.
Skills you'll practice
Details to know
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About this Guided Project
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:
Understand the main components of neural networks in machine learning
Train your first neural network for image classification
Improve neural network accuracy through hidden layers and different optimizers
Practice Activity: Fine tune a neural network and improve its accuracy
Visualize training data and performance of the model
Create a convolutional neural network with Conv2D and MaxPooling2D
Reduce overfitting with BatchNormalization, Dropout, and L2 regularization
Practice Activity: Create alternative neural network models to reduce overfitting
CIFAR-10 Classification Challenge
Recommended experience
Basic familiarity with Python. In particular, importing libraries, defining variables, arrays, functions, and classes, and creating plots.
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Instructor
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Frequently asked questions
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.