Chapter 2 Answer

[Pavan Outlaw]

Ithought in conjunction with the course, we could add the answers to the questionnaire for the fastbook chapters for people who are struggling. I have posted the questions here. @jeremy if you think this is a good idea, could we make this post a wiki so we could all add the answers?

GPU stands for Graphics Processing Unit (also known as a graphics card). Standard computers have various components like CPUs, RAM, etc. CPUs, or central processing units, are the core units of all standard computers, and they execute the instructions that make up computer programs. GPUs, on the other hand, are specialized units meant for displaying graphics, especially the 3D graphics in modern computer games. The hardware optimizations used in GPUs allow it to handle thousands of tasks at the same time. Incidentally, these optimizations allow us to run and train neural networks hundreds of times faster than a regular CPU.

In a Jupyter Notebook, we can create code cells and run code in an interactive manner. When we execute a cell containing some code (in this case: 1+1), the code is run by Python and the output is displayed underneath the code cell (in this case: 2).

For us humans, it is easy to identify images in a photos, such as identifying cats vs dogs in a photo. This is because, subconsciously our brains have learned which features define a cat or a dog for example. But it is hard to define set rules for a traditional computer program to recognize a cat or a dog. Can you think of a universal rule to determine if a photo contains a cat or dog? How would you encode that as a computer program? This is very difficult because cats, dogs, or other objects, have a wide variety of shapes, textures, colors, and other features, and it is close to impossible to manually encode this in a traditional computer program.

The universal approximation theorem states that neural networks can theoretically represent any mathematical function. However, it is important to realize that practically, due to the limits of available data and computer hardware, it is impossible to practically train a model to do so. But we can get very close!

You will need an architecture for the given problem. You will need data to input to your model. For most use-cases of deep learning, you will need labels for your data to compare your model predictions to. You will need a loss function that will quantitatively measure the performance of your model. And you need a way to update the parameters of the model in order to improve its performance (this is known as an optimizer).

In a predictive policing model, we might end up with a positive feedback loop, leading to a highly biased model with little predictive power. For example, we may want a model that would predict crimes, but we use information on arrests as a proxy . However, this data itself is slightly biased due to the biases in existing policing processes. Training with this data leads to a biased model. Law enforcement might use the model to determine where to focus police activity, increasing arrests in those areas. These additional arrests would be used in training future iterations of models, leading to an even more biased model. This cycle continues as a positive feedback loop

Pretrained models have been trained on other problems that may be quite similar to the current task. For example, pretrained image recognition models were often trained on the ImageNet dataset, which has 1000 classes focused on a lot of different types of visual objects. Pretrained models are useful because they have already learned how to handle a lot of simple features like edge and color detection. However, since the model was trained for a different task than already used, this model cannot be used as is.

However, a lot of information can be represented as images . For example, a sound can be converted into a spectrogram, which is a visual interpretation of the audio. Time series (ex: financial data) can be converted to image by plotting on a graph. Even better, there are various transformations that generate images from time series, and have achieved good results for time series classification. There are many other examples, and by being creative, it may be possible to formulate your problem as an image classification problem, and use pretrained image models to obtain state-of-the-art results!

At its core, segmentation is a pixelwise classification problem. We attempt to predict a label for every single pixel in the image. This provides a mask for which parts of the image correspond to the given label.

Training models requires various other parameters that define how the model is trained. For example, we need to define how long we train for, or what learning rate (how fast the model parameters are allowed to change) is used. These sorts of parameters are hyperparameters.

About point 16, what about saying that you need metrics to quantitatively measure the performance, and moving the loss function in the part about updating the parameters, together with the optimizer? I think it is more relevant to the latter (and with point 24).

In point 20, the hyperparameters are mentioned before defining them in point 32. Furthermore, maybe it could be nice to explicitly state the difference with the parameters no matter having previously defined them.

You will need an architecture for the given problem. You will need data to input to your model. You will need labels for your data to compare your model predictions to. You will need a loss function that will quantitatively measure the performance of your model. And you need a way to update the parameters of the model in order to improve its performance (this is known as an optimizer).

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