From the course: Building Recommender Systems with Machine Learning and AI
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Handwriting recognition with Keras - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Handwriting recognition with Keras
Let's go back to the Jupyter notebook, make sure that you're in the rexus environment first, and import the Keras.ipynb file from the deep learning intro folder. Once it's uploaded, select it to fire up that notebook. Again, Keras is just a higher level API in TensorFlow that makes deep learning a lot easier. We'll start off by importing all the stuff we need, assembling the layers of a neural network. We're going to import the Dense and Dropout layers as well, so we can actually add some new things into this neural network to make it even better, and prevent overfitting. Finally, we will import the RMSprop optimizer, which is what we're going to use for our gradient ascent. Hit shift enter to process that block. Let's go ahead and load up the mnist dataset that we used in the previous example. Keras's version is a little bit different, it actually has 60,000 training samples, as opposed to 55,000, but still 10,000 test samples. That's just a one line operation. Next we need to…
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Contents
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Deep learning introduction1m 30s
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Deep learning prerequisites8m 13s
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History of artificial neural networks10m 51s
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Playing with TensorFlow12m 2s
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Training neural networks5m 47s
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Tuning neural networks3m 52s
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Introduction to TensorFlow11m 29s
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Handwriting recognition with TensorFlow, part 113m 18s
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Handwriting recognition with TensorFlow, part 212m 3s
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Introduction to Keras2m 48s
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Handwriting recognition with Keras9m 52s
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Classifier patterns with Keras3m 58s
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Predict political parties of politicians with Keras9m 55s
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Intro to convolutional neural networks (CNNs)8m 59s
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CNN architectures2m 54s
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Handwriting recognition with CNNs8m 38s
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Intro to recurrent neural networks (RNNs)7m 38s
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Training recurrent neural networks3m 21s
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Sentiment analysis of movie reviews using RNNs and Keras11m 1s
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