From the course: Building Recommender Systems with Machine Learning and AI
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Tuning neural networks - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Tuning neural networks
- Let's talk about overfitting as well. You can see you'll often end up with patterns like this, where you're not really getting a clean solution. You end up with these weird spikes sometimes and sometimes if you let things go a little bit too long, it ends up reinforcing those spikes. Those overfitted areas where you're not really fitting to the pattern you're looking for, you're just fitting to the training data that you were given. Obviously if you have thousands of weights to tune, those connections between each neuron and each layer of your neurons can add up really quickly. So it's very easy for overfitting to happen, but fortunately there are ways to deal with it. One is called early stopping. So as soon as you see performance start to drop, that might be nature's way of telling you that it might be time for you to stop learning. At this point maybe you're just overfitting. There are also regularization terms you can add to the cost function during training which are like the…
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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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