Machine Learning Guide
This series aims to teach you the high level fundamentals of machine learning from A to Z. I'll teach you the basic intuition, algorithms, and math. We'll discuss languages and frameworks, deep learning, and more. Audio may be an inferior medium to task; but with all our exercise, commute, and chores hours of the day, not having an audio supplementary education would be a missed opportunity. And where your other resources will provide you the machine learning trees, I’ll provide the forest. Additionally, consider me your syllabus. At the end of every episode I’ll provide the best-of-the-best resources curated from around the web for you to learn each episode’s details.

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25. Convolutional Neural Networks

Convnets or CNNs. Filters, feature maps, window/stride/padding, max-pooling.

## Resources
- Stanford cs231n: Convnets ( `course:medium`
- Hands-On Machine Learning with Scikit-Learn and TensorFlow ( `book:medium`
- The usual DL resources (pick one):
** Deep Learning Book ( (Free HTML version ( `book:hard` comprehensive DL bible; highly mathematical
** ( `course:medium` practical DL for coders
** Neural Networks and Deep Learning ( `book:medium` shorter online "book"

## Episode

- One-time donations w/ BTC / PayPal
- Image recognition, classification - computer vision
** ML takeover
** Final main network (MLP, RNN, CNN)
- Don't use MLP for images, use CNNs
- Filters -> feature maps -> convolutional layers
- Window, stride, padding
- Max-pooling
- Architectures (ILSVRC ImageNet Challenge)
** LeNet-5
** AlexNet
** GoogLeNet
** Inception
** Resnet
** etc..

2017-10-30 13:47:01 UTC

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