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Questions and Answers
The size of our output layer depends on the number of __________________________ that we want our neural network to guess.
The size of our output layer depends on the number of __________________________ that we want our neural network to guess.
categories
How many output neurons will this network have?
How many output neurons will this network have?
- 52 (correct)
- 1,300 (correct)
- 26 (correct)
- 130 (correct)
What number does the program print for every epoch?
What number does the program print for every epoch?
loss value
You want to see this number go __________ over time.
You want to see this number go __________ over time.
What is the tradeoff if you add more layers and neurons to your network to make it more accurate?
What is the tradeoff if you add more layers and neurons to your network to make it more accurate?
Where are the images for this step being stored?
Where are the images for this step being stored?
Does the program process the story correctly?
Does the program process the story correctly?
What was added to the code?
What was added to the code?
Does the program work after the changes were made?
Does the program work after the changes were made?
Which of the following use A.I.? (Circle all that apply)
Which of the following use A.I.? (Circle all that apply)
How do you feel about A.I.?
How do you feel about A.I.?
Writers like to imagine a more _________________________ A.I.
Writers like to imagine a more _________________________ A.I.
This is a realistic way to think of A.I.
This is a realistic way to think of A.I.
A machine is said to have artificial intelligence if it can _____________________________, potentially __________________ from the data, and use that knowledge to _____________ and achieve specific goals.
A machine is said to have artificial intelligence if it can _____________________________, potentially __________________ from the data, and use that knowledge to _____________ and achieve specific goals.
Today's A.I. still can't do most of the things that humans do.
Today's A.I. still can't do most of the things that humans do.
A.I. is used to screen ____________________ applications.
A.I. is used to screen ____________________ applications.
When was the term 'artificial intelligence' coined?
When was the term 'artificial intelligence' coined?
What important conference is responsible for a lot of advancements in A.I.?
What important conference is responsible for a lot of advancements in A.I.?
A.I. has developed SLOWER than these early thinkers predicted.
A.I. has developed SLOWER than these early thinkers predicted.
When did the 'A.I. Winter' end?
When did the 'A.I. Winter' end?
What 'two big developments' helped to spur the A.I. revolution forward?
What 'two big developments' helped to spur the A.I. revolution forward?
What increases the speed of a computer?
What increases the speed of a computer?
When was the first iPhone released?
When was the first iPhone released?
Scientists were inspired by the human brain when they made some kinds of A.I.
Scientists were inspired by the human brain when they made some kinds of A.I.
What are the three basic parts of a neuron?
What are the three basic parts of a neuron?
What machine did Rosenblatt build and what was he trying to teach it to do?
What machine did Rosenblatt build and what was he trying to teach it to do?
What special weight represents the threshold?
What special weight represents the threshold?
Generalized A.I. was successfully created in the early 2010’s.
Generalized A.I. was successfully created in the early 2010’s.
Name 2 different activation functions.
Name 2 different activation functions.
At the beginning of supervised learning, the A.I. makes random guesses.
At the beginning of supervised learning, the A.I. makes random guesses.
When John Green Bot is correct, the update rule will add __ to the weight.
When John Green Bot is correct, the update rule will add __ to the weight.
John Green Bot learns from __.
John Green Bot learns from __.
You can put the results of a supervised learning test into “a table, called a __.”
You can put the results of a supervised learning test into “a table, called a __.”
What is the key to most A.I. challenges?
What is the key to most A.I. challenges?
How many neurons does our brain have?
How many neurons does our brain have?
Who helped to develop A.I. that could recognize images?
Who helped to develop A.I. that could recognize images?
What was their international dataset called, and how many images does it have?
What was their international dataset called, and how many images does it have?
What made AlexNet different?
What made AlexNet different?
What layer mathematically combines the final hidden layer outputs?
What layer mathematically combines the final hidden layer outputs?
What is the key to neural networks and all of A.I.?
What is the key to neural networks and all of A.I.?
Every hidden neuron looks for all of the components needed to determine if the picture is what they are looking for.
Every hidden neuron looks for all of the components needed to determine if the picture is what they are looking for.
How many neurons are in the output layer?
How many neurons are in the output layer?
Deeper neural networks are networks with more hidden layers to do deep learning.
Deeper neural networks are networks with more hidden layers to do deep learning.
What is one way that neural networks are used in your life already?
What is one way that neural networks are used in your life already?
Neural networks are now big enough that some can do tasks better than humans.
Neural networks are now big enough that some can do tasks better than humans.
What algorithm do neural networks use to handle mistakes?
What algorithm do neural networks use to handle mistakes?
What are the two main parts of neural networks?
What are the two main parts of neural networks?
The task of finding the best weights for a neural network architecture is called __.
The task of finding the best weights for a neural network architecture is called __.
What is the goal of linear regression?
What is the goal of linear regression?
What do neurons multiply the given features by?
What do neurons multiply the given features by?
What represents the error when the predicted answer and the correct answer is more than just one number?
What represents the error when the predicted answer and the correct answer is more than just one number?
What algorithm did scientists create to help neural networks learn?
What algorithm did scientists create to help neural networks learn?
A local optimal solution is where the weights make the error relatively small, but not the smallest it could be.
A local optimal solution is where the weights make the error relatively small, but not the smallest it could be.
Ideally, you should explore different solutions at the same time, on the same neural network. However, that just isn’t feasible yet.
Ideally, you should explore different solutions at the same time, on the same neural network. However, that just isn’t feasible yet.
Over time, backpropagation will adjust the neuron’s weights, so that the neural networks output matches the training data. That’s called __.
Over time, backpropagation will adjust the neuron’s weights, so that the neural networks output matches the training data. That’s called __.
What language will be used to write the code in this episode?
What language will be used to write the code in this episode?
Where can you find the code that the episode is working on today?
Where can you find the code that the episode is working on today?
Step 1 includes training the network with 100,000 images.
Step 1 includes training the network with 100,000 images.
What dataset (abbreviated) will be used for this lab?
What dataset (abbreviated) will be used for this lab?
What type of neural network will they be using in their episode?
What type of neural network will they be using in their episode?
How many output neurons will this network have?
How many output neurons will this network have?
What number does the program print for every epoch?
What number does the program print for every epoch?
What is the tradeoff if you add more layers and neurons to your network to make it more accurate?
What is the tradeoff if you add more layers and neurons to your network to make it more accurate?
Where are the images for this step being stored?
Where are the images for this step being stored?
Does the program process the story correctly?
Does the program process the story correctly?
What was added to the code?
What was added to the code?
Does the program work after the changes were made?
Does the program work after the changes were made?
Which of the following use A.I.? (Circle all that apply)
Which of the following use A.I.? (Circle all that apply)
How do you feel about A.I.?
How do you feel about A.I.?
Writers like to imagine a more ______ A.I.
Writers like to imagine a more ______ A.I.
This is a realistic way to think of A.I.
This is a realistic way to think of A.I.
A machine is said to have artificial intelligence if it can ______, potentially ______ from the data, and use that knowledge to ______ and achieve specific goals.
A machine is said to have artificial intelligence if it can ______, potentially ______ from the data, and use that knowledge to ______ and achieve specific goals.
Today's A.I. still can't do most of the things that humans do.
Today's A.I. still can't do most of the things that humans do.
A.I. is used to screen ______ applications.
A.I. is used to screen ______ applications.
When was the term 'artificial intelligence' coined?
When was the term 'artificial intelligence' coined?
What important conference is responsible for a lot of advancements in A.I.?
What important conference is responsible for a lot of advancements in A.I.?
A.I. has developed FASTER / SLOWER than these early thinkers predicted.
A.I. has developed FASTER / SLOWER than these early thinkers predicted.
When did the 'A.I. Winter' end?
When did the 'A.I. Winter' end?
What 'two big developments' helped to spur the A.I. revolution forward? 1. Increase in ______ 2. ______ and ______.
What 'two big developments' helped to spur the A.I. revolution forward? 1. Increase in ______ 2. ______ and ______.
What increases the speed of a computer?
What increases the speed of a computer?
When was the first iPhone released?
When was the first iPhone released?
What process helps computers learn?
What process helps computers learn?
What are the 'three main types of learning'?
What are the 'three main types of learning'?
Supervised learning is the process of learning with ______.
Supervised learning is the process of learning with ______.
What two things does an A.I. need to learn?
What two things does an A.I. need to learn?
Scientists were inspired by the human brain when they made some kinds of A.I.
Scientists were inspired by the human brain when they made some kinds of A.I.
What are the three basic parts of a neuron?
What are the three basic parts of a neuron?
What machine did Rosenblatt build? What was he trying to teach it to do?
What machine did Rosenblatt build? What was he trying to teach it to do?
What special weight represents the threshold?
What special weight represents the threshold?
Generalized A.I. was successfully created in the early 2010s.
Generalized A.I. was successfully created in the early 2010s.
Name 2 different activation functions.
Name 2 different activation functions.
At the beginning of supervised learning, the A.I. makes random guesses.
At the beginning of supervised learning, the A.I. makes random guesses.
When John Green Bot is correct, the update rule will add ______ to the weight.
When John Green Bot is correct, the update rule will add ______ to the weight.
John Green Bot learns from ______.
John Green Bot learns from ______.
You can put the results of a supervised learning test into 'a table, called a ______.
You can put the results of a supervised learning test into 'a table, called a ______.
______ tells us how much you should ______ your program when it says it’s found something.
______ tells us how much you should ______ your program when it says it’s found something.
______ tells you how much your program can find of the thing you’re looking for.
______ tells you how much your program can find of the thing you’re looking for.
What is the 'key to most A.I. challenges?
What is the 'key to most A.I. challenges?
How many neurons does our brain have?
How many neurons does our brain have?
Not that long ago, a big challenge in A.I. was ______ recognition.
Not that long ago, a big challenge in A.I. was ______ recognition.
Who helped to develop A.I. that could recognize images?
Who helped to develop A.I. that could recognize images?
What was their international dataset called? How many images does it have?
What was their international dataset called? How many images does it have?
What made AlexNet different?
What made AlexNet different?
All neural networks are made up of an ______ layer, an ______ layer, and any number of ______ layers in between.
All neural networks are made up of an ______ layer, an ______ layer, and any number of ______ layers in between.
Each pixel can be represented with 3 numbers that correspond to the amount of ______, ______, and ______ in the pixel.
Each pixel can be represented with 3 numbers that correspond to the amount of ______, ______, and ______ in the pixel.
What layer is 'where the final hidden layer outputs are mathematically combined to answer the problem?'
What layer is 'where the final hidden layer outputs are mathematically combined to answer the problem?'
What is 'the key to neural networks and…all of A.I.'?
What is 'the key to neural networks and…all of A.I.'?
Every hidden neuron looks for all of the components needed to determine if the picture is what they are looking for.
Every hidden neuron looks for all of the components needed to determine if the picture is what they are looking for.
How many neurons are in the output layer?
How many neurons are in the output layer?
Deeper neural networks 'are networks with more ______ to do deep ______.'
Deeper neural networks 'are networks with more ______ to do deep ______.'
What is one way that neural networks are used in your life already?
What is one way that neural networks are used in your life already?
Neural networks are now big enough that some can do tasks better than humans.
Neural networks are now big enough that some can do tasks better than humans.
What algorithm do neural networks use to handle mistakes?
What algorithm do neural networks use to handle mistakes?
What are the 'two main parts' of neural networks?
What are the 'two main parts' of neural networks?
The task of finding the best weights for a neural network architecture is called [blank].
The task of finding the best weights for a neural network architecture is called [blank].
What is 'the goal of linear regression?'
What is 'the goal of linear regression?'
By connecting together many simple ______ with ______, a neural network can learn to solve complicated ______.
By connecting together many simple ______ with ______, a neural network can learn to solve complicated ______.
What do neurons multiply the given features by?
What do neurons multiply the given features by?
What represents the error when 'the predicted answer and the correct answer is more than just one number?'
What represents the error when 'the predicted answer and the correct answer is more than just one number?'
What algorithm did scientists create 'to help neural networks learn?'
What algorithm did scientists create 'to help neural networks learn?'
A ______ is 'where the weights make the error relatively small, but not the smallest it could be.'
A ______ is 'where the weights make the error relatively small, but not the smallest it could be.'
Ideally, you should explore different solutions at the same time, on the same neural network. However, that just isn’t feasible yet.
Ideally, you should explore different solutions at the same time, on the same neural network. However, that just isn’t feasible yet.
______ will adjust the neuron’s ______, so that the neural networks' output matches the ______.
______ will adjust the neuron’s ______, so that the neural networks' output matches the ______.
______ is a very important step to training a good neural network.
______ is a very important step to training a good neural network.
What language will be used to write the code in this episode?
What language will be used to write the code in this episode?
Where can you find the code that the episode is working on today?
Where can you find the code that the episode is working on today?
Our goal is to program a neural network to recognize ______ letters and convert them to ______.
Our goal is to program a neural network to recognize ______ letters and convert them to ______.
Usually with a project like this, we’d have to write code to figure out where one letter ends and another begins...That’s called the ______.
Usually with a project like this, we’d have to write code to figure out where one letter ends and another begins...That’s called the ______.
Step 1: 'Find or create a ______ to train our neural network.'
Step 1: 'Find or create a ______ to train our neural network.'
Step 2: 'Create a ______.'
Step 2: 'Create a ______.'
Step 3: 'Train, ______, and tweak our code until we feel that it’s ______ enough.'
Step 3: 'Train, ______, and tweak our code until we feel that it’s ______ enough.'
Step 1.2 includes training the network with 100,000 images.
Step 1.2 includes training the network with 100,000 images.
What dataset (abbreviated) will be used for this lab?
What dataset (abbreviated) will be used for this lab?
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Study Notes
What is Artificial Intelligence?
- Artificial intelligence (AI) is used in many fields, including banking, healthcare, technology, and communication.
- AI is often portrayed as a more advanced intelligence than humans, but this is a common misrepresentation.
- AI is defined as a machine's ability to learn from data, and use that knowledge to make decisions and achieve goals.
- AI often focuses on performing specific tasks, rather than mimicking human capabilities.
- The term "artificial intelligence" was coined in 1956.
- The Dartmouth Summer Research Project on Artificial Intelligence was a pivotal conference that significantly advanced the field.
- The development of AI has been slower than early thinkers anticipated.
- The field of AI experienced a "winter" period, where progress slowed down considerably before seeing a resurgence due to improvements in computing power and the availability of big data.
Supervised Learning
- AI systems learn through various processes, including supervised learning.
- Supervised learning involves teaching the system with labeled data, providing the system with both input and corresponding output.
- Supervised learning relies on having both data and an answer key.
- The core component of machine learning is the neuron.
- Neurons consist of dendrites, a cell body, and an axon.
- One of the first AI projects was Rosenblatt's Perceptron, which aimed to teach a system to recognize patterns.
- Activation functions, like the sigmoid and ReLU, determine a neuron's activation based on the weighted sum of its inputs.
- Supervised learning uses a process of updating weights to improve accuracy, adjusting them based on success or failure.
- Supervised learning results are organized in a confusion matrix, which helps evaluate performance.
- Precision and recall are important metrics for evaluating the performance of supervised learning models.
- The availability and quality of data are essential for training effective AI systems.
What is Artificial Intelligence?
- A.I. is used in many areas, such as banks, YouTube, and apps for cell phones
- A machine is said to have artificial intelligence if it can interpret data, potentially learn from the data, and use that knowledge to adapt and achieve specific goals.
- The term “artificial intelligence” was coined in 1956.
- The Dartmouth Summer Research Project was responsible for many advancements in A.I.
- The “A.I. Winter” ended around 2010.
- The A.I. revolution was spurred on by the increase in computing power and the rise of the internet and social media.
- More transistors increase the speed of a computer.
- The first iPhone was released in 2007.
Supervised Learning
- Supervised learning is a way for computers to learn. Other types of learning include unsupervised and reinforcement learning.
- Supervised learning is the process of learning with training labels.
- An A.I. needs computing power and data to learn.
- Scientists used the structure of neurons in the human brain to create certain types of A.I.
- Neuron parts include dendrites, a cell body, and an axon.
- The Perception was a machine built by Rosenblatt and designed to classify shapes as triangles or not triangles.
- Bias is a special weight representing the threshold.
- At the beginning of supervised learning, the A.I. makes random guesses.
- John Green Bot is a system that learns by receiving updates based on its performance.
- When John Green Bot is correct, the update rule assigns a weight of 0.
- John Green Bot learns from its failures.
- Supervised learning test results can be recorded in a Confusion Matrix.
- Precision indicates the trustworthiness of an artificial intelligence system's output.
- Recall measures the percentage of correct results.
- The key to many A.I. challenges is "figuring out what criteria to use".
Neural Networks and Deep Learning
- Our brain has approximately 100 billion neurons.
- Image recognition was a major challenge for A.I.
- Fei-Fei Li contributed to the development of A.I. capable of image recognition.
- ImageNet is a dataset of 3.2 million images.
- AlexNet is a specific neural network that used many hidden layers and faster hardware.
- Every neural network has an input layer, an output layer, and any number of hidden layers.
- Each pixel can be represented by three numbers: red, green, and blue.
- The output layer is where the final hidden layer outputs are combined to solve the problem.
- Math is the key to neural networks and AI.
- Deeper neural networks have more hidden layers to perform deep learning.
Training Neural Networks
- Neural networks are approaching or exceeding human capabilities in certain tasks.
- Backpropagation is an algorithm commonly used for training neural networks to handle errors.
- The two main components of neural networks are learning and adjusting the weights.
- Backpropagation is the process of finding the optimal weights for a neural network architecture.
- Linear regression seeks to identify a linear relationship between input and output parameters.
- Complex problems can be solved by connecting simple neurons using weights.
- Neurons multiply given features by specific weights.
- The error, in cases where the predicted and correct answers differ by more than one number, is represented by the “loss” of the neural network.
- Backpropagation is an algorithm designed to optimize the learning process of neural networks.
- When the weights result in a relatively small but not minimal error, it's called a "local minimum."
- Backpropagation fine-tunes the neuron’s weights to achieve a desired output, aligning with the training data. This process is called “gradient descent.”
Neural Networks
- Neural networks utilize backpropagation to handle mistakes.
- Two main parts of neural networks: architecture and weights.
- The process of finding the best weights for a neural network architecture is called optimization.
- The goal of linear regression is to adjust the line to minimize error.
- Neurons multiply given features by their assigned weights.
- A Loss Function represents the error when the predicted answer and the correct answer differ by more than one number.
- Backpropagation of the Error is an algorithm created to help neural networks learn.
- A local optimal solution is where the weights minimize error, but not to the smallest possible value.
- Backpropagation adjusts neuron weights over time to match the output to training data, a process called fitting to the training data.
Crash Course Artificial Intelligence: How to Make an AI Read Handwriting (LAB)
- The episode focuses on programming a neural network to recognize handwritten letters and convert them to typed text.
- The code for the episode is written in Python and can be found in the video's description.
- The process involves segmentation, which is figuring out where one letter ends and another begins.
- The lab uses the EMNIST dataset.
- The episode uses a multi-layer perceptron neural network (MLP).
- The size of the output layer depends on the number of label types the network needs to guess.
- The network has 26 output neurons, representing the letters of the alphabet.
- During every epoch, the program prints the Error of the Loss Function, which should decrease over time.
- Adding more layers and neurons to a network for accuracy leads to slower performance.
- Images for the lab are stored in a GitHub repository.
- The initial attempt at handwriting recognition fails.
- The code is improved by applying filters, centering images, and resizing them to 28 x 28 pixels.
- The improved code successfully recognizes most of the handwriting.
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