From the course: Building Recommender Systems with Machine Learning and AI
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Deep learning prerequisites - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Deep learning prerequisites
there are a few algorithms you need to understand first as they are important concepts of how deep learning works. One of these pre-requisites is stochastic gradient decent or SGD. That's the same as GD we talked about when we covered matrix factorization. As I mentioned it's possible to implement matrix factorization using neural networks that SGD depends on with neural networks, and we'll cover softmax which is used at the allputive neural networks to translate their raw outputs into more useful rating classifications. The first thing we want to talk about is gradient descent, this is basically a machine learning optimization technique for trying to find the most optimal set of parameters for a given problem. So what we're plotting here is basically some sort of cost function, some measurement of the error of your learning system. This applies to machine learning in general. You're going to have some sort of function that defines how close your predicted values are to the actual…
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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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