<div style='margin-bottom: 20px;'> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the total number of weights and biases in the network described?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Almost exactly 13,000.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does the process of learning in neural networks refer to?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Finding a valid setting for all the weights and biases to solve the problem at hand.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">Why might manually setting weights and biases be considered both fun and horrifying?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Because it involves purposefully tweaking thousands of numbers to achieve desired layer functions.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How does organizing weight matrices and vectors simplify communication of activations?</h2> <p style="font-weight: normal; font-size: 1.2rem;">It allows for the full transition of activations from one layer to the next to be communicated in an extremely tight and neat little expression.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the purpose of weights in a neural network?</h2> <p style="font-weight: normal; font-size: 1.2rem;">To determine how activations from one layer influence the activations in the next layer.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How are the weights organized in relation to the activations?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The weights are organized as a matrix, where each row corresponds to the connections between one layer and a particular neuron in the next layer.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How many weights are there in a hidden layer of 16 neurons connected to 784 pixel neurons?</h2> <p style="font-weight: normal; font-size: 1.2rem;">12,544 (784 times 16).</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How many hidden layers are chosen for the network, and how many neurons do they each have?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Two hidden layers, each with 16 neurons.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does a positive input to the sigmoid function result in?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Close to 1.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the network's 'choice' based on the brightest neuron in the output layer?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The digit the image represents</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">Why is it significant that the brain can recognize a '3' despite low resolution and varying pixel values?</h2> <p style="font-weight: normal; font-size: 1.2rem;">It demonstrates the brain's remarkable ability to interpret and recognize patterns effortlessly.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the desired outcome when computing a weighted sum of pixel values in the context of neural networks?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The desired outcome is for activations to be some value between 0 and 1.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the primary inspiration behind neural networks?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The brain</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What will be shown in the next video, as mentioned?</h2> <p style="font-weight: normal; font-size: 1.2rem;">How the network learns the appropriate weights and biases just by looking at data, and more about what the network is really doing.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What analogy is used to explain the process of parsing speech?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Taking raw audio and picking out distinct sounds, which combine to form syllables, words, phrases, and abstract thoughts.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">Which function is commonly used to transform a weighted sum into a value between 0 and 1?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The sigmoid function.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does a very negative input to the sigmoid function result in?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Close to 0.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does taking the weighted sum of the activations in the first layer represent?</h2> <p style="font-weight: normal; font-size: 1.2rem;">It corresponds to one of the terms in the matrix vector product.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What example is used to introduce neural networks?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Recognizing handwritten digits.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How many neurons correspond to the 28x28 pixel input image?</h2> <p style="font-weight: normal; font-size: 1.2rem;">784 neurons</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What do the activations in the neurons of the last layer represent?</h2> <p style="font-weight: normal; font-size: 1.2rem;">How much the system thinks a given image corresponds with a given digit.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does the activation of a neuron represent in the context of an input image?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The grayscale value of the corresponding pixel.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the purpose of adding a bias to the weighted sum before applying the sigmoid function?</h2> <p style="font-weight: normal; font-size: 1.2rem;">To determine how high the weighted sum needs to be before the neuron starts getting meaningfully active.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does the author want to demonstrate with neural networks?</h2> <p style="font-weight: normal; font-size: 1.2rem;">How a neural network functions, assuming no background knowledge, and to visualize its operations beyond being a buzzword.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How does ReLU function in terms of activation?</h2> <p style="font-weight: normal; font-size: 1.2rem;">It takes the max of zero and a given value, acting as the identity function if it passes a certain threshold, or zero if it does not.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the resolution of the image used to recognize the digit '3'?</h2> <p style="font-weight: normal; font-size: 1.2rem;">28x28 pixels</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">Why is a good grasp of linear algebra important in machine learning?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Much of machine learning comes down to having a good grasp of linear algebra.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How is the addition of the bias to the matrix vector product represented?</h2> <p style="font-weight: normal; font-size: 1.2rem;">By organizing all the biases into a vector and adding the entire vector to the previous matrix vector product.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the goal of having a layered structure in neural networks?</h2> <p style="font-weight: normal; font-size: 1.2rem;">To break down complex recognition tasks into layers of abstraction.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What function did early networks use to squish the relevant weighted sum into the interval between zero and one?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The sigmoid function.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is suggested for a visual understanding of matrices and matrix vector multiplication?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Taking a look at the series on linear algebra, especially chapter 3.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What optimization do many libraries provide that benefits machine learning code?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Many libraries optimize matrix multiplication.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How might the network recognize a digit like '9'?</h2> <p style="font-weight: normal; font-size: 1.2rem;">By recognizing specific little edges that make up the upper loop and a long vertical line.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What function is wrapped around the matrix vector product as a final step?</h2> <p style="font-weight: normal; font-size: 1.2rem;">A sigmoid function.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What range of values can a neuron's activation hold?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Between 0 and 1</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How should the activations from one layer be organized for processing?</h2> <p style="font-weight: normal; font-size: 1.2rem;">The activations from one layer should be organized into a column as a vector.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What benefit does understanding weights and biases offer when a network doesn't perform as expected?</h2> <p style="font-weight: normal; font-size: 1.2rem;">It provides a starting place for experimenting with changes to improve the structure.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What function has become more popular than sigmoid in modern networks?</h2> <p style="font-weight: normal; font-size: 1.2rem;">ReLU (rectified linear unit).</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How does the network determine the activations of the next layer?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Activations in one layer determine the activations of the next layer.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">Why is it reassuring that the network looks complicated?</h2> <p style="font-weight: normal; font-size: 1.2rem;">If it were any simpler, there would be little hope that it could take on the challenge of recognizing digits.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does ReLU stand for?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Rectified Linear Unit.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">Why did the use of sigmoids become less favorable for training networks?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Sigmoids were difficult to train at some point, and ReLU happened to work very well for incredibly deep neural networks.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How can a neuron in the second layer be made to detect an edge in a specific region?</h2> <p style="font-weight: normal; font-size: 1.2rem;">By assigning positive weights to connections in the region of interest and computing their weighted sum.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">How can neurons be more accurately thought of, according to the text?</h2> <p style="font-weight: normal; font-size: 1.2rem;">As functions that take in the outputs of all the neurons in the previous layer and spit out a number between 0 and 1.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What does the entire network function as?</h2> <p style="font-weight: normal; font-size: 1.2rem;">A function that takes in 784 numbers as an input and spits out 10 numbers as an output.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What makes the network's function complicated?</h2> <p style="font-weight: normal; font-size: 1.2rem;">It involves 13,000 parameters in the forms of weights and biases, picks up on certain patterns, and involves iterating many matrix vector products and the sigmoid function.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What subcomponents might the neurons in the second to last layer correspond to?</h2> <p style="font-weight: normal; font-size: 1.2rem;">Specific parts of digits, like a loop or a line.</p> </div> <div style="margin-bottom: 10px; background-color: #f2f2f2; border-radius: 1rem; padding: 10px 20px;"> <h2 style="font-weight: bold; margin-bottom: 3px; font-size: 1.5rem;">What is the purpose of subscribing to the channel, as suggested?</h2> <p style="font-weight: normal; font-size: 1.2rem;">So that the neural networks that underlie YouTube's recommendation algorithm are primed to believe that you want to see content from this channel get recommended to you.</p> </div> </div>