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AI-For-Beginners
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Merge pull request #2 from microsoft/main 

getting upstream changes
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3
.github/workflows/docsify.yml vendored
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@ -1,3 +1,4 @@
name: docs
on: [push]
@ -12,4 +13,4 @@ jobs:
github_token: ${{ secrets.GP_TOKEN }}
dir: etc/docsify
# destination branch
branch: "main"

2
3-NeuralNetworks/04-OwnFramework/README.md
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@ -58,7 +58,7 @@ Note that the left-most part of all those expressions is the same, and thus we c
<img alt="compute graph" src="images/ComputeGraphGrad.png"/>
> TODO: image citation
> Image by author
> ✅ We will cover backprop in much more detail in our notebook example.

1803
4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb
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1381
4-ComputerVision/10-GANs/StyleTransfer.ipynb
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18
etc/quiz-app/src/assets/translations/en/index.js
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@ -1,11 +1,7 @@
import en1 from "./lesson-1.json";
import en2 from "./lesson-2.json";
import en3 from "./lesson-3.json";
const quiz = {
0: en1[0],
1: en2[0],
2: en3[0]
};
export default quiz;
import x1 from "./lesson-1.json";
import x2 from "./lesson-2.json";
import x3 from "./lesson-3.json";
import x4 from "./lesson-4.json";
import x5 from "./lesson-5.json";
const quiz = { 0 : x1[0], 1 : x2[0], 2 : x3[0], 3 : x4[0], 4 : x5[0] };
export default quiz;

48
etc/quiz-app/src/assets/translations/en/lesson-1.json
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@ -5,7 +5,7 @@
"error": "Sorry, try again",
"quizzes": [
{
"id": 1,
"id": 101,
"title": "Introduction to AI - Pre Quiz",
"quiz": [
{
@ -13,15 +13,15 @@
"answerOptions": [
{
"answerText": "Charles Barkley",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "Charles Babbage",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "Charles Darwin",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -30,11 +30,11 @@
"answerOptions": [
{
"answerText": "True",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "False",
"isCorrect": "true"
"isCorrect": true
}
]
},
@ -43,51 +43,51 @@
"answerOptions": [
{
"answerText": "false, they are usually designed by a series of rules.",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "true, they are usually considered to be 'intelligent'.",
"isCorrect": "false"
"answerText": "true, they are usually considered to be 'intelligent",
"isCorrect": false
},
{
"answerText": "false, but they are increasingly able to pass Turing tests as they become more sophisticated.",
"isCorrect": "true"
"isCorrect": true
}
]
}
]
},
{
"id": 2,
"title": "Introduction to AI: Post-Quiz",
"id": 201,
"title": "Introduction to AI - Post-Quiz",
"quiz": [
{
"questionText": "A top-down approach to AI is a model of reasoning called",
"answerOptions": [
{
"answerText": "strategic reasoning",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "symbolic reasoning",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "synergistic reasoning",
"isCorrect": "false"
"isCorrect": false
}
]
},
{
"questionText": "A bottom-up approach to AI might leverage a neural network to",
"questionText": "A bottom-up approach to AI is based on neural networks",
"answerOptions": [
{
"answerText": "true",
"isCorrect": "true"
"answerText": "True",
"isCorrect": true
},
{
"answerText": "false",
"isCorrect": "false"
"answerText": "False",
"isCorrect": false
}
]
},
@ -96,15 +96,15 @@
"answerOptions": [
{
"answerText": "1950s",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "1960s",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "1970s",
"isCorrect": "true"
"isCorrect": true
}
]
}
@ -112,4 +112,4 @@
}
]
}
]
]

43
etc/quiz-app/src/assets/translations/en/lesson-2.json
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@ -4,9 +4,8 @@
"complete": "Congratulations, you completed the quiz!",
"error": "Sorry, try again",
"quizzes": [
{
"id": 3,
"id": 102,
"title": "Knowledge Representation and Expert Systems: Pre-Quiz",
"quiz": [
{
@ -14,15 +13,15 @@
"answerOptions": [
{
"answerText": "knowledge seeking and reading",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "knowledge representation and reasoning",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "knowledge reasoning and seeking",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -30,12 +29,12 @@
"questionText": "Knowledge is the same as information",
"answerOptions": [
{
"answerText": "true",
"isCorrect": "false"
"answerText": "True",
"isCorrect": false
},
{
"answerText": "false",
"isCorrect": "true"
"answerText": "False",
"isCorrect": true
}
]
},
@ -44,22 +43,22 @@
"answerOptions": [
{
"answerText": "active learning process",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "passive learning process",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "both of these",
"isCorrect": "false"
"isCorrect": false
}
]
}
]
},
{
"id": 4,
"id": 202,
"title": "Knowledge Representation and Expert Systems: Post-Quiz",
"quiz": [
{
@ -67,15 +66,15 @@
"answerOptions": [
{
"answerText": "algorithmic",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "symbolic",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "synergistic",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -84,11 +83,11 @@
"answerOptions": [
{
"answerText": "true",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "false",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -97,15 +96,15 @@
"answerOptions": [
{
"answerText": "executes a reasoning loop",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "looks for a goal",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "starts over",
"isCorrect": "false"
"isCorrect": false
}
]
}
@ -113,4 +112,4 @@
}
]
}
]
]

43
etc/quiz-app/src/assets/translations/en/lesson-3.json
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@ -4,25 +4,24 @@
"complete": "Congratulations, you completed the quiz!",
"error": "Sorry, try again",
"quizzes": [
{
"id": 5,
"title": "Introduction to Neural Networks - Perceptron: Post-Quiz",
"id": 103,
"title": "Introduction to Neural Networks - Perceptron: Pre-Quiz",
"quiz": [
{
"questionText": "Early neural networks required",
"answerOptions": [
{
"answerText": "manual weight adjusting",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "terabytes of data",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "special reasoning",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -31,11 +30,11 @@
"answerOptions": [
{
"answerText": "true",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "false",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -43,23 +42,23 @@
"questionText": "A perceptron is a ___ type of model",
"answerOptions": [
{
"answerText": "linear regression",
"isCorrect": "false"
"answerText": "multi-class classification",
"isCorrect": false
},
{
"answerText": "clustering",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "binary classification",
"isCorrect": "true"
"isCorrect": true
}
]
}
]
},
{
"id": 6,
"id": 203,
"title": "Introduction to Neural Networks - Perceptron: Post-Quiz",
"quiz": [
{
@ -67,15 +66,15 @@
"answerOptions": [
{
"answerText": "size",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "error",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "nodes",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -84,11 +83,11 @@
"answerOptions": [
{
"answerText": "true",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "false",
"isCorrect": "false"
"isCorrect": false
}
]
},
@ -97,15 +96,15 @@
"answerOptions": [
{
"answerText": "learning rate",
"isCorrect": "false"
"isCorrect": false
},
{
"answerText": "weights",
"isCorrect": "true"
"isCorrect": true
},
{
"answerText": "gradient",
"isCorrect": "false"
"isCorrect": false
}
]
}
@ -113,4 +112,4 @@
}
]
}
]
]

122
etc/quiz-app/src/assets/translations/en/lesson-4.json
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@ -4,7 +4,125 @@
"complete": "Congratulations, you completed the quiz!",
"error": "Sorry, try again",
"quizzes": [
{
"id": 104,
"title": "Neural Networks - Pre Quiz",
"quiz": [
{
"questionText": "The quality of prediction is measured by Loss function",
"answerOptions": [
{
"answerText": "True",
"isCorrect": true
},
{
"answerText": "False",
"isCorrect": false
}
]
},
{
"questionText": "One layer network is capable of classifying ____",
"answerOptions": [
{
"answerText": "linearly joined classes",
"isCorrect": false
},
{
"answerText": "linearly separable classes",
"isCorrect": true
},
{
"answerText": "single layers of classes",
"isCorrect": false
}
]
},
{
"questionText": "The method of training multi-layered perceptron is called ____",
"answerOptions": [
{
"answerText": "back propagation",
"isCorrect": true
},
{
"answerText": "multiple propagation",
"isCorrect": false
},
{
"answerText": "front propagation",
"isCorrect": false
}
]
}
]
},
{
"id": 204,
"title": "Neural Networks - Post Quiz",
"quiz": [
{
"questionText": "We use ____ for regression loss functions",
"answerOptions": [
{
"answerText": "absolute error",
"isCorrect": false
},
{
"answerText": "mean squared error",
"isCorrect": false
},
{
"answerText": "all of the above",
"isCorrect": true
}
]
},
{
"questionText": "All but one is a type of classification loss function",
"answerOptions": [
{
"answerText": "0-1 loss",
"isCorrect": false
},
{
"answerText": "binary loss",
"isCorrect": true
},
{
"answerText": "logistic loss",
"isCorrect": false
}
]
},
{
"questionText": "Softmax can be used to convert inputs into probabilities",
"answerOptions": [
{
"answerText": "True",
"isCorrect": false
},
{
"answerText": "False",
"isCorrect": true
}
]
},
{
"questionText": "Cross-entropy loss is a function that can calculate similarity between two arbitrary probability distributions",
"answerOptions": [
{
"answerText": "True",
"isCorrect": true
},
{
"answerText": "False",
"isCorrect": false
}
]
}
]
}
]
}
]
]

128
etc/quiz-app/src/assets/translations/en/lesson-5.json Normal file
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@ -0,0 +1,128 @@
[
{
"title": "AI for Beginners: Quizzes",
"complete": "Congratulations, you completed the quiz!",
"error": "Sorry, try again",
"quizzes": [
{
"id": 105,
"title": "Frameworks - Pre Quiz",
"quiz": [
{
"questionText": "Deep Neural Network training requires a lot of computations",
"answerOptions": [
{
"answerText": "True",
"isCorrect": true
},
{
"answerText": "False",
"isCorrect": false
}
]
},
{
"questionText": "Overfitting occurs because of ____",
"answerOptions": [
{
"answerText": "Not enough testing data",
"isCorrect": false
},
{
"answerText": "Too powerful model",
"isCorrect": true
},
{
"answerText": "Too much noise in output data",
"isCorrect": false
}
]
},
{
"questionText": "Bias errors are caused by our ____ not being able to capture the relationship between training data correctly.",
"answerOptions": [
{
"answerText": "model",
"isCorrect": false
},
{
"answerText": "algorithm",
"isCorrect": true
},
{
"answerText": "computer",
"isCorrect": false
}
]
}
]
},
{
"id": 205,
"title": "Frameworks - Post Quiz",
"quiz": [
{
"questionText": "After compiling our model object, we train by calling ____ function",
"answerOptions": [
{
"answerText": "fit",
"isCorrect": true
},
{
"answerText": "train",
"isCorrect": false
},
{
"answerText": "teach",
"isCorrect": false
}
]
},
{
"questionText": "Binary cross-entropy is also called log loss",
"answerOptions": [
{
"answerText": "True",
"isCorrect": true
},
{
"answerText": "False",
"isCorrect": false
}
]
},
{
"questionText": "Tensorflow is to ____ while PyTorch is to ____",
"answerOptions": [
{
"answerText": "Facebook, Google",
"isCorrect": false
},
{
"answerText": "Google, Facebook",
"isCorrect": true
},
{
"answerText": "Microsoft, Google",
"isCorrect": false
}
]
},
{
"questionText": "Pred is the values predicted by the network",
"answerOptions": [
{
"answerText": "True",
"isCorrect": true
},
{
"answerText": "False",
"isCorrect": false
}
]
}
]
}
]
}
]

135
etc/quiz-src/questions-en.txt Normal file
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@ -0,0 +1,135 @@
Lesson 1B Introduction to AI - Pre Quiz
* A famous 19th century proto-computer engineer was
- Charles Barkley
+ Charles Babbage
- Charles Darwin
* Weak AI is a system designed to solve many tasks
- True
+ False
* Chat bots are an example of truly intelligent systems
- false, they are usually designed by a series of rules.
- true, they are usually considered to be 'intelligent
+ false, but they are increasingly able to pass Turing tests as they become more sophisticated.
Lesson 1E Introduction to AI - Post-Quiz
* A top-down approach to AI is a model of reasoning called
- strategic reasoning
+ symbolic reasoning
- synergistic reasoning
* A bottom-up approach to AI is based on neural networks
+ True
- False
* The AI Winter occurred in this era
- 1950s
- 1960s
+ 1970s
Lesson 2B Knowledge Representation and Expert Systems: Pre-Quiz
* The top-down approach to creating intelligent systems was based on:
- knowledge seeking and reading
+ knowledge representation and reasoning
- knowledge reasoning and seeking
* Knowledge is the same as information
- True
+ False
* Knowledge is obtained by an:
+ active learning process
- passive learning process
- both of these
Lesson 2E Knowledge Representation and Expert Systems: Post-Quiz
* The simplest method of knowledge representation is:
+ algorithmic
- symbolic
- synergistic
* Scenarios can represent complex situations that can unfold in time
+ true
- false
* Forward inference starts with initial data and then:
+ executes a reasoning loop
- looks for a goal
- starts over
Lesson 3B Introduction to Neural Networks - Perceptron: Pre-Quiz
* Early neural networks required
+ manual weight adjusting
- terabytes of data
- special reasoning
* A simple neuron is also called a 'threshold logic unit'
+ true
- false
* A perceptron is a ___ type of model
- multi-class classification
- clustering
+ binary classification
Lesson 3E Introduction to Neural Networks - Perceptron: Post-Quiz
* To train a perceptron, find a weights vector that results in the smallest ___.
- size
+ error
- nodes
* To minimize the function of weights, you can use gradient descent
+ true
- false
* During gradient descent, each step updates the ___
- learning rate
+ weights
- gradient
Lesson 4B Neural Networks - Pre Quiz
* The quality of prediction is measured by Loss function
+ True
- False
* One layer network is capable of classifying ____
- linearly joined classes
+ linearly separable classes
- single layers of classes
* The method of training multi-layered perceptron is called ____
+ back propagation
- multiple propagation
- front propagation
Lesson 4E Neural Networks - Post Quiz
* We use ____ for regression loss functions
- absolute error
- mean squared error
+ all of the above
* All but one is a type of classification loss function
- 0-1 loss
+ binary loss
- logistic loss
* Softmax can be used to convert inputs into probabilities
- True
+ False
* Cross-entropy loss is a function that can calculate similarity between two arbitrary probability distributions
+ True
- False
Lesson 5B Frameworks - Pre Quiz
* Deep Neural Network training requires a lot of computations
+ True
- False
* Overfitting occurs because of ____
- Not enough testing data
+ Too powerful model
- Too much noise in output data
* Bias errors are caused by our ____ not being able to capture the relationship between training data correctly.
- model
+ algorithm
- computer
Lesson 5E Frameworks - Post Quiz
* After compiling our model object, we train by calling ____ function
+ fit
- train
- teach
* Binary cross-entropy is also called log loss
+ True
- False
* Tensorflow is to ____ while PyTorch is to ____
- Facebook, Google
+ Google, Facebook
- Microsoft, Google
* Pred is the values predicted by the network
+ True
- False

62
etc/quiz-src/qzmkjson.py Normal file
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@ -0,0 +1,62 @@
src = 'questions-en.txt'
dst_dir = '../quiz-app/src/assets/translations/en'
import json,os
from copy import deepcopy
from matplotlib.cbook import ls_mapper
def mk_id(s): # convert from 4B/4E to numeric lesson id
lesson_no = int(s[:-1])
return lesson_no + (100 if s[-1]=='B' else 200)
with open('template.json') as f:
doc = json.load(f)
inp = open(src,encoding='utf-8').readlines()
prev_q = None
prev_l = None
prev_l_id = None
lessons = { }
for l in inp:
l = l.strip()
if l=='': continue
if l.startswith('+') or l.startswith('-'): # answer
prev_q['answerOptions'].append({ "answerText" : l[2:], "isCorrect" : l.startswith('+') })
elif l.startswith('*'):
if prev_q:
prev_l['quiz'].append(prev_q)
prev_q = { "questionText" : l[2:], "answerOptions" : [] };
elif l.startswith('Lesson'):
if prev_q:
prev_l['quiz'].append(prev_q)
prev_q = None
if prev_l is not None:
lessons[prev_l_id] = prev_l
prev_l_id = l[7:l.find(' ',7)]
prev_l = { "id" : mk_id(prev_l_id), "title" : l[l.find(' ',7)+1:], "quiz" : [] }
else:
print(f"Error: {l}")
prev_l['quiz'].append(prev_q)
lessons[prev_l_id] = prev_l
lesson_content = {}
for k,v in lessons.items():
no = int(k[:-1])
if no not in lesson_content.keys():
lesson_content[no] = deepcopy(doc)
lesson_content[no][0]['quizzes'].append(v)
with open(os.path.join(dst_dir,'index.js'),'w',encoding='utf-8') as f:
for i,k in enumerate(lesson_content.keys()):
f.write(f'import x{k} from "./lesson-{k}.json";\n')
t = ', '.join([ f"{i} : x{k}[0]" for i,k in enumerate(lesson_content.keys())]);
f.write(f"const quiz = {{ {t} }}; \n");
f.write("export default quiz;")
for k,v in lesson_content.items():
with open(os.path.join(dst_dir,f"lesson-{k}.json"),'w', encoding='utf-8') as f:
json.dump(v,f,indent=2)

9
etc/quiz-src/template.json Normal file
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@ -0,0 +1,9 @@
[
{
"title": "AI for Beginners: Quizzes",
"complete": "Congratulations, you completed the quiz!",
"error": "Sorry, try again",
"quizzes": [
]
}
]

1
index.html Normal file
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@ -0,0 +1 @@
<meta http-equiv="refresh" content="0; url=https://github.com/jlooper/AI-For-Beginners/tree/main/etc/docsify">