fastAI Questionnaire
感覺還挺多的，怪不得說每一課要額外8小時進行學習。

1.Do you need these for deep learning?
Lots of math T / F
Lots of data T / F
Lots of expensive computers T / F
A PhD T / F
F F F F

2.Name five areas where deep learning is now the best in the world.
自然語言學習（NLP），計算機視覺（CV）,醫學（Medicine），生物（Biology），圖像生成（Image generation)，推薦系統（Recommendation systems）

3.What was the name of the first device that was based on the principle of the artificial neuron?
The Mark I Perceptron

4.Based on the book of the same name, what are the requirements for parallel distributed processing (PDP)?
A set of processing units
A state of activation
An output function for each unit
A pattern of connectivity among units
A propagation rule for propagating patterns of activities through the network of connectivities
An activation rule for combining the inputs impinging on a unit with the current state of that unit to produce an output for the unit
A learning rule whereby patterns of connectivity are modified by experience
An environment within which the system must operate

5.What were the two theoretical misunderstandings that held back the field of neural networks?
In the 1980’s most models were built with a second layer of neurons, thus avoiding the problem that had been identified by Minsky and Papert (this was their “pattern of connectivity among units,” to use the framework above). And indeed, neural networks were widely used during the '80s and '90s for real, practical projects. However, again a misunderstanding of the theoretical issues held back the field. In theory, adding just one extra layer of neurons was enough to allow any mathematical function to be approximated with these neural networks, but in practice such networks were often too big and too slow to be useful.

6.What is a GPU?
Graphics Processing Unit (GPU): Also known as a graphics card. A special kind of processor in your computer that can handle thousands of single tasks at the same time, especially designed for displaying 3D environments on a computer for playing games. These same basic tasks are very similar to what neural networks do, such that GPUs can run neural networks hundreds of times faster than regular CPUs. All modern computers contain a GPU, but few contain the right kind of GPU necessary for deep learning.

7.Open a notebook and execute a cell containing: 1+1. What happens?
2

8.Follow through each cell of the stripped version of the notebook for this chapter. Before executing each cell, guess what will happen.
完成

9.Complete the Jupyter Notebook online appendix.
沒找到哪里有appendix

10.Why is it hard to use a traditional computer program to recognize images in a photo?
傳統意義上來說，我們一般寫一個函數，從輸出得到結果，這種方式無法應用于圖像識別

11.What did Samuel mean by “weight assignment”?
Weights are just variables, and a weight assignment is a particular choice of values for those variables. The program’s inputs are values that it processes in order to produce its results—for instance, taking image pixels as inputs, and returning the classification “dog” as a result. The program’s weight assignments are other values that define how the program will operate.

12.What term do we normally use in deep learning for what Samuel called “weights”?
model parameters

13.Draw a picture that summarizes Samuel’s view of a machine learning model.

14.Why is it hard to understand why a deep learning model makes a particular prediction?
一般程序寫好了之后，重復運行應該得到同樣的結果，但是深度學習的結果可能存在不同。

15.What is the name of the theorem that shows that a neural network can solve any mathematical problem to any level of accuracy?
萬能近似定理（the universal approximation theorem）

16.What do you need in order to train a model?
A model cannot be created without data.
A model can only learn to operate on the patterns seen in the input data used to train it.
This learning approach only creates predictions, not recommended actions.
It’s not enough to just have examples of input data; we need labels for that data too

17.How could a feedback loop impact the rollout of a predictive policing model?
A predictive policing model is created based on where arrests have been made in the past. In practice, this is not actually predicting crime, but rather predicting arrests, and is therefore partially simply reflecting biases in existing policing processes.
Law enforcement officers then might use that model to decide where to focus their police activity, resulting in increased arrests in those areas.
Data on these additional arrests would then be fed back in to retrain future versions of the model.

18.Do we always have to use 224×224-pixel images with the cat recognition model?
不是，提高分辨率可能會增加精度，但是會增加計算量

19.What is the difference between classification and regression?
Classification and regression have very specific meanings in machine learning. These are the two main types of model that we will be investigating in this book. A classification model is one which attempts to predict a class, or category. That is, it’s predicting from a number of discrete possibilities, such as “dog” or “cat.” A regression model is one which attempts to predict one or more numeric quantities, such as a temperature or a location.

20.What is a validation set? What is a test set? Why do we need them?
The validation set is the portion of the dataset that is not used for training the model, but for evaluating the model during training, in order to prevent overfitting. This ensures that the model performance is not due to “cheating” or memorization of the dataset, but rather because it learns the appropriate features to use for prediction. However, it is possible that we overfit the validation data as well. This is because the human modeler is also part of the training process, adjusting hyperparameters and training procedures according to the validation performance. Therefore, another unseen portion of the dataset, the test set, is used for final evaluation of the model. This splitting of the dataset is necessary to ensure that the model generalizes to unseen data.

21.What will fastai do if you don’t provide a validation set?
fastai會自動創建，默認為20%

22.Can we always use a random sample for a validation set? Why or why not?
不能，對于有些類型的數據，隨機選擇可能并不好，比如有時間相關性的數據，也許分段會更好。

23.What is overfitting? Provide an example.
過擬合，很好地擬合了已有數據，但是缺乏一般性，比如用高階函數擬合二次函數

24.What is a metric? How does it differ from “loss”?
A metric is a function that measures the quality of the model’s predictions using the validation set, and will be printed at the end of each epoch.
The concept of a metric may remind you of loss, but there is an important distinction. The entire purpose of loss is to define a “measure of performance” that the training system can use to update weights automatically. In other words, a good choice for loss is a choice that is easy for stochastic gradient descent to use. But a metric is defined for human consumption, so a good metric is one that is easy for you to understand, and that hews as closely as possible to what you want the model to do. At times, you might decide that the loss function is a suitable metric, but that is not necessarily the case.

25.How can pretrained models help?
A model that has weights that have already been trained on some other dataset is called a pretrained model. You should nearly always use a pretrained model, because it means that your model, before you’ve even shown it any of your data, is already very capable. And, as you’ll see, in a deep learning model many of these capabilities are things you’ll need, almost regardless of the details of your project. For instance, parts of pretrained models will handle edge, gradient, and color detection, which are needed for many tasks.
預訓練模型可以較少計算量，提高效率，從一個較高的起點開始訓練

26.What is the “head” of a model?
The head of a model is the part that is newly added to be specific to the new dataset. An epoch is one complete pass through the dataset. After calling fit, the results after each epoch are printed, showing the epoch number, the training and validation set losses (the “measure of performance” used for training the model), and any metrics you’ve requested (error rate, in this case).

27.What kinds of features do the early layers of a CNN find? How about the later layers?
early layers:represent diagonal, horizontal, and vertical edges, as well as various different gradients.
later layers:the features are now able to identify and match with higher-level semantic components, such as car wheels, text, and flower petals.

28.Are image models only useful for photos?
No.An image recognizer can, as its name suggests, only recognize images. But a lot of things can be represented as images, which means that an image recogniser can learn to complete many tasks.

29.What is an “architecture”?
The template of the model that we’re trying to fit; the actual mathematical function that we’re passing the input data and parameters to

30.What is segmentation?
Creating a model that can recognize the content of every individual pixel in an image is called segmentation.

31.What is y_range used for? When do we need it?
用于預測連續型的

32.What are “hyperparameters”?
In realistic scenarios we rarely build a model just by training its weight parameters once. Instead, we are likely to explore many versions of a model through various modeling choices regarding network architecture, learning rates, data augmentation strategies, and other factors we will discuss in upcoming chapters. Many of these choices can be described as choices of hyperparameters. The word reflects that they are parameters about parameters, since they are the higher-level choices that govern the meaning of the weight parameters.

33.What’s the best way to avoid failures when using AI in an organization
Make sure a training, validation, and testing set is defined properly in order to evaluate the model in an appropriate manner.
Try out a simple baseline, which future models should hopefully beat. Or even this simple baseline may be enough in some cases.

反復看了幾遍，又看了看書，感覺講得真的很容易聽懂，尤其是講發展歷史講得很好，問題的設置感覺也很合理，和那種動不動就證明求導過程的舒服多了。
最后一題找了半天沒找到，最后論壇搜索抄了一下。

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