This 45-minute project-based course on Coursera's Rhyme platform will teach you how to build and train a multilayer perceptron (MLP) model using Keras and Tensorflow. You will work with the Reuters dataset, a set of short newswires and their topics, and learn to classify them into 46 different mutually-exclusive topics. With the cloud desktop pre-configured with Python, Jupyter, and Keras, you can focus on learning and get instant access to the software and data you need. This course is best suited for learners based in North America, with access to the cloud desktop 5 times.
Facial Expression Recognition with Keras
2.0
Coursera218 learners
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This project-based course on Coursera's Rhyme platform will teach you how to build and train a convolutional neural network (CNN) in Keras to recognize facial expressions. You will use OpenCV to detect faces in images and draw bounding boxes around them. After training, saving, and exporting the CNN, you will directly serve the trained model to a web interface and perform real-time facial expression recognition on video and image data. With instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed, you can focus on learning and get the most out of this course. This course is best suited for learners based in North America.
Image Denoising Using AutoEncoders in Keras and Python
2.0
Coursera101 learners
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This 1-hour long project-based course will teach you how to use Autoencoders in Keras and Python to denoise images. You will learn how to import key libraries, dataset and visualize images, perform image normalization, pre-processing, and add random noise to images. You will also build an Autoencoder using Keras with Tensorflow 2.0 as a backend, compile and fit Autoencoder model to training data, and assess the performance of trained Autoencoder using various KPIs. This course is best suited for learners based in the North America region.
Creating Custom Callbacks in Keras
2.5
Coursera214 learners
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This 1.5-hour long project-based course on Coursera's Rhyme platform will teach you how to create a custom callback function in Keras and use it during a model training process. You will learn to write a log file, plot the training metrics in a graph, and reduce the learning rate during the training with each epoch. With pre-configured cloud desktops containing all the software and data you need, you can focus on learning without worrying about the setup. Prerequisites include familiarity with Python, Neural Networks, and the Keras framework. This course is best suited for learners based in North America.
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