- 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 lecture