5. Transcript - Issues and Techniques in Deep Learning 2 - 28012024
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Questions and Answers

What type of distribution are the weights initially chosen from?

  • Uniform distribution
  • Normal distribution
  • Exponential distribution
  • Gaussian distribution (correct)
  • How many neurons are there in the layer with 10 neurons?

  • 14
  • 6
  • 10 (correct)
  • 9
  • Which activation function is initially used for explanation purposes?

  • Tanh
  • Sigmoid (correct)
  • Linear
  • ReLU
  • What do the weights in the neural network layer essentially do to the currents?

    <p>Amplify the currents</p> Signup and view all the answers

    What does Speaker 2 express concern about regarding computational cycles?

    <p>Starting at random places in the search space</p> Signup and view all the answers

    How many nodes are mentioned in the second layer?

    <p>4</p> Signup and view all the answers

    According to Dr. Anand Jayaraman, what is he planning to provide around the concept of starting at random places?

    <p>Nuance</p> Signup and view all the answers

    What is one reason given in the text for considering alternatives to choosing weights from a Gaussian distribution?

    <p>Better convergence rates</p> Signup and view all the answers

    How does Dr. Anand Jayaraman describe his feelings towards the intuition being discussed?

    <p>He loves it</p> Signup and view all the answers

    Which of the following is NOT mentioned as a type of neuron activation function used for explanation purposes?

    <p>ELU</p> Signup and view all the answers

    Which activity does Dr. Anand Jayaraman compare the innovations in deep learning to?

    <p>Being a cricket fan</p> Signup and view all the answers

    What is the term used for the connections between neurons from one layer to another in the neural network?

    <p>Synapses</p> Signup and view all the answers

    What does Dr. Anand Jayaraman apologize for regarding the use of cricket analogies?

    <p>Making analogies that are difficult to understand</p> Signup and view all the answers

    In what context does Dr. Anand Jayaraman mention that not everyone can be lucky?

    <p>Understanding cricket analogies</p> Signup and view all the answers

    'A lack of advancements in deep learning is similar to what, according to Dr. Anand Jayaraman?' What is the appropriate answer?

    <p>'Progress in incremental steps'</p> Signup and view all the answers

    'What might people be missing out on if they are unfamiliar with cricket analogies?' Which option best completes this question?

    <p>'The fun and enjoyment associated with cricket'</p> Signup and view all the answers

    What topic did Speaker 5 discuss with their family?

    <p>God and evolution</p> Signup and view all the answers

    According to Dr. Anand Jayaraman, what is the problem now?

    <p>Being more than neck deep</p> Signup and view all the answers

    What is Dr. Anand Jayaraman's academic background?

    <p>PhD in physics</p> Signup and view all the answers

    What type of questions arise during the study of science according to Dr. Anand Jayaraman?

    <p>Questions about existence of God</p> Signup and view all the answers

    What does Dr. Anand Jayaraman find fascinating while studying neuroscience?

    <p>Studying the brain</p> Signup and view all the answers

    What activity does Dr. Anand Jayaraman describe as 'playing God'?

    <p>Building machines</p> Signup and view all the answers

    What question arises when working on AI according to Dr. Anand Jayaraman?

    <p>'Could this have happened by chance?'</p> Signup and view all the answers

    'Whether you want to or not, you know, you have a religious upbringing' - Who is Dr. Anand Jayaraman referring to with this statement?

    <p>'You'</p> Signup and view all the answers

    What is the main reason for using learning rate decay in practice?

    <p>To speed up the overall amount of learning</p> Signup and view all the answers

    Why do practitioners start with a high learning rate when implementing learning rate decay?

    <p>To take quicker steps when far from the minimum</p> Signup and view all the answers

    What happens to the learning rate as the algorithm gets closer to the minimum during learning rate decay?

    <p>It decreases</p> Signup and view all the answers

    How does adjusting the learning rate affect the loss function during optimization?

    <p>It decreases the loss function</p> Signup and view all the answers

    What is the purpose of cutting the learning rate in learning rate decay after some time?

    <p>To slow down and approach the final minimum point accurately</p> Signup and view all the answers

    How does using a learning rate that is too big impact the optimization process?

    <p>It leads to suboptimal minima due to overshooting</p> Signup and view all the answers

    What benefit does adjusting the learning rate offer as the optimization algorithm approaches the final minimum?

    <p>It speeds up convergence dramatically</p> Signup and view all the answers

    When implementing learning rate decay, what happens to the size of steps taken as you get closer to your final target?

    <p>They decrease in size</p> Signup and view all the answers

    What is the purpose of initializing the weights in a neural network?

    <p>To set the right magnitude of the weights for efficient optimization</p> Signup and view all the answers

    Why is it important to set the right magnitude of weights in a neural network?

    <p>To avoid taking small steps forever during optimization</p> Signup and view all the answers

    According to Dr. Anand Jayaraman, why are the initial weights likely to be small?

    <p>To prevent taking small steps forever before reaching better solutions</p> Signup and view all the answers

    How does setting the right magnitude of weights contribute to optimization?

    <p>By aiding the model in converging efficiently to better solutions</p> Signup and view all the answers

    Why does Dr. Anand Jayaraman emphasize starting from a specific corner on the surface?

    <p>To avoid taking small steps forever before reaching the minimum point</p> Signup and view all the answers

    What does Dr. Anand Jayaraman mean when he mentions setting 'the right magnitude of the weights'?

    <p>Ensuring the scale of weights is appropriate for the neural network task</p> Signup and view all the answers

    In what way do small initial weights help in neural network training?

    <p>Preventing slow convergence by avoiding small steps forever</p> Signup and view all the answers

    How does setting different magnitudes of weights in initial layers versus later layers benefit neural networks?

    <p>Allows for fine-tuning and hierarchical learning in the network</p> Signup and view all the answers

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