Deep Learning Interview Prep Course
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Published 2024-01-30
✏️ Course created by Tatev Karen Aslanyan.
✏️ Expanded course with 100 questions: academy.lunartech.ai/product/deep-learning-intervi…
⭐️ 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?
All Comments (21)
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"Big thanks to the team of FreeCodeCamp, and especially to Beau for this incredible opportunity to collaborate on this AI content. ❤It's a privilege to contribute to worlds leading and most accessible online coding platforms, that shapes coding education and industry. Looking forward to more collaborations in the future!" Tatev Aslanyan
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Finally a interview prep video other than Full stack Development.
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I love this. Thinking about applying for another job but always nervous when doing interviews. I’ve always gotten hired but still would love to hit them with a woah factor during interview. I feel like with the skills I have and the blown out interview would help me with me negotiating my salary
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AWESOME!!!!!!! Interview preparation is a whole process that involves a lot skills, beside technical skill you need to know how to transmit your knowledge in a clear and effective way. Thank you for this and for all the FCC fantastic content!
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This was invaluable for interview prep! Not just the tech, but the communication tips really resonate. Thanks for all the FCC gems!
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This is just amazing, im having a interview this next week, and this course will be my todo of the weekend. Thanks a lot Tatever and FreeCodeCamp
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Could you please consider creating a video discussing computer vision interview questions?
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3:05:36 small erratum - gpt style models are decoder only and bert model (sentiment analysis) architecture is encoder only. Btw, great stuff. Have a nice one.
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ALSO REQ FOR IMP TOPICS IN DATA SCIENCE TOO
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Most attractive thumbnail of freecodecamp 🥴
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Please make a course on machine learning for data science interview prepration.
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Thank ypu for making a lot of helpful Stuff free ❤
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Thank you so much, really helpful. I'd correct a mistake: 47:00 the leaky ReLU should not be for but , and to generalize, that can be any number between 0 and 1.
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It's very clearly made vedio and make sure it helpful to us for clearing any interview
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Thank you the lectures. for Answer 13, my understanding is reversed.
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Thanks for this wonderful stuff
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This is interesting and a nice refresher. Is there one for machine learning in the works?
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In Answer 7, as shown in the chart on the right, shouldnt the formula be 'F(z) = 0.01z' for the negative case?
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She's so brave!
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She is so excellent but why she doesn't have a YouTube channel?
