Deep Learning Interview Prep Course

Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations. ✏️ Course created by Tatev Karen Aslanyan. ✏️ Expanded course with 100 questions: https://academy.lunartech.ai/product/deep-learning-interview-preparation-course-100-q-as ❤️ Try interactive Career courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Career (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ ⌨️ 0:00:00 Introduction ⌨️ 0:08:20 Question 1: What is Deep Learning? ⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning? ⌨️ 0:15:25 Question 3: What is a Neural Network? ⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning ⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way ⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network? ⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them ⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network? ⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works ⌨️ 0:53:45 Question 10: What is Gradient Descent? ⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning? ⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning? ⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent? ⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN ⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN ⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason? ⌨️ 1:37:50 Question 17: What do you understand by a computational graph? ⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning? ⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry? ⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems? ⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks? ⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima? ⌨️ 2:03:38 Question 23: How is GD different from SGD? ⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term? ⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent. ⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)? ⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model? ⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages? ⌨️ 2:34:12 Question 29: What is RMSProp and how does it work? ⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods ⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs? ⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam? ⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN? ⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN? ⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN? ⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN? ⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN? ⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients? ⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients? ⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights? ⌨️ 3:29:18 Question 41: What is Dropout and how does it work? ⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN? ⌨️ 3:35:06 Question 43: Is Dropout like Random Forest? ⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing? ⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN? ⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN? ⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN? ⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI? ⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality? ⌨️ 3:56:47 Question 50: What are Generative Models, give examples?