From the course: Building Recommender Systems with Machine Learning and AI
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Training neural networks - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Training neural networks
- [Instructor] Alright I know you are probably itching to dive into some code by now, but there's a little more theory we need to cover with deep learning. I want to talk a little bit about exactly how they are trained, and some tips for tuning them now that you've had a little bit of hands on experience with them using the TensorFlow playground. How do you train a multi-layer perceptron? Well, it's using a technique called backpropagation. It's not that complicated really at a conceptual level, all we're doing is gradient descent like we talked about before using that mathematical trick of reverse-mode autodiff to make it happen efficiently. For each training step we just compute the output error for the weights that we have currently in place for each connection between each artificial neuron and then this is where backpropagation happens. Since there are multiple layers to deal with we have to take that error that's computed at the end of our neural network and back-propagate it…
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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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