[Download this notebook](12 - Autoencoders.ipynb)
I did my best to minimize the training time. I had saved a model that was trained already. However, the server does not play along. Therefore it is best to just do something else for an hour when training. You don’t have to train 100 epochs, you can end the training early via Kernel > Interrupt Kernel. In this lesson you’ll learn: what an autoencoder is.
[Download this notebook](10 - Transfer Learning.ipynb)
In this lesson you’ll learn: what is meant by transfer learning. how to load and customize pre-trained models. how to read images from folders into Python. Last week you developed a CNN that can recognize numbers. Today, we focus on taking advantage of pre-trained neural networks. First, you will train a model that can distinguish between various breeds of dogs and cats. In the training task, you will use ResNet to detect pneumonia in X-ray images.
[Download this notebook](09 - Convolutional Neural Network.ipynb)
This week we are looking at convolutional neural networks. Convolutional neural networks (CNNs) are used primarily, but not exclusively, for image recognition.
Unlike the neural networks we have seen so far, CNNs can read images as a matrix. This means that local context is not lost by flattening the image.
Otavio Good. 2017 “A Visual and Intuitive Understanding of Deep Learning” O’Reilly AI Conference import torch from torch import nn import pandas as pd import numpy as np from matplotlib import pyplot as plt from torch.
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Author: Oliver Mai
As presented in this YouTube video by Phil Tabor
Gym Environment: LunarLander-v2 This environment is inspired by a subfield of Optimal Control: rocket trajectory optimization. Sadly the documentation is a bit lacking, but we will briefly talk about features of this environment.
In “LunarLander-v2” the agent (or human player) controls a spacecraft, which is supposed to be landed on a planetary surface. The lander can only be moved in a 2D plane (Note: This environment requires the 2D physics engine “Box2d”, which can be installed by pip install -e '.
[Download this notebook](13 - Graph Neural Networks.ipynb)
In this lesson you’ll learn: how molecules can be represented as graphs. how basic Graph Neural Networks work. how to write a Graph Neural Network as a Pytorch class. Graph Neural Networks are still a relatively new class of algorithms. Intuitively, molecules can be represented very easily as a (mathematical) graph. The bonds of a molecule correspond to the edges of the graph and the atoms to the nodes.
[Download this notebook](08 - PyTorch.ipynb)
In this lesson you’ll learn: how to program a simple neural net using PyTorch. how to implement more advanced layers in your neural net (Dropout, Batchnorm). about more advanced optimisation (Momentum, adam). Last week you programmed a simple neural network yourself. As mentioned earlier, it is not necessary to program every net yourself. Certain software packages take care of many of the inconveniences of creating and training nets “by hand”.
[Download this notebook](11 - Recurrent Neural Networks.ipynb)
Recurrent Neural Networks (RNNs) are another special form of neural networks. RNNs are mainly used for sequences that are arranged in a fixed order. In these cases, the order of the individual elements of the sequence is often crucial for the interpretation of the whole sequence.
Languages as a classical example lend themselves immediately. This is because the ordering of a sentence influences the interpretation of the individual words.