Introduction
  • Q1 - What is Deep Learning
  • Q2 - How does Deep Learning differ from traditional Machine Learning?
  • Q3 - What is a Neural Network?
  • Q4 - Explain the concept of a neuron in Deep Learning.
  • Q5 - Explain architecture of Neural Networks in simple way
  • Q6 - What is an activation function in a Neural Network?
  • Q7 - Name few popular activation functions and describe them
  • Q8 - What happens if you do not use any activation functions in a NN?
  • Q9 - Describe how training of basic Neural Networks works
  • Q10 - What is Gradient Descent?
  • Q11 - What is the function of an optimizer in Deep Learning?
  • Q12 - What is backpropagation, and why is it important in Deep Learning?
  • Q13 - How is backpropagation different from gradient descent?
  • Q14 - Describe what Vanishing Gradient Problem is and it’s impact on NN
  • Q15 - Describe what Exploding Gradients Problem is and it’s impact on NN
  • Q16 - There is a neuron results in a large error in backpropagation. Reason?
  • Q17 - What do you understand by a computational graph?
  • Q18 - What is Loss Function and what are various Loss functions used in DL?
  • Q19 - What is Cross Entropy loss function and how is it called in industry?
Bonus section
  • Bonus lecture