Урок 1. 00:04:07
Q1 - What is Deep Learning?
Урок 2. 00:04:14
Q2 - What is Deep Learning?
Урок 3. 00:07:14
Q3 - What is a Neural Network?
Урок 4. 00:03:34
Q4 - Explain the concept of a neuron in Deep Learning.
Урок 5. 00:07:53
Q5 - Explain architecture of Neural Networks in simple way
Урок 6. 00:04:01
Q6 - What is an activation function in a Neural Network?
Урок 7. 00:13:43
Q7 - Name few popular activation functions and describe them
Урок 8. 00:01:27
Q8 - What happens if you do not use any activation functions in a NN?
Урок 9. 00:05:53
Q9 - Describe how training of basic Neural Networks works
Урок 10. 00:10:41
Q10 - What is Gradient Descent?
Урок 11. 00:05:34
Q11 - What is the function of an optimizer in Deep Learning?
Урок 12. 00:08:39
Q12 - What is backpropagation, and why is it important in Deep Learning?
Урок 13. 00:03:05
Q13 - How is backpropagation different from gradient descent?
Урок 14. 00:07:01
Q14 - Describe what Vanishing Gradient Problem is and it’s impact on NN
Урок 15. 00:08:31
Q15 - Describe what Exploding Gradients Problem is and it’s impact on NN
Урок 16. 00:04:40
Q16 - There is a neuron results in a large error in backpropagation. Reason?
Урок 17. 00:06:18
Q17 - What do you understand by a computational graph?
Урок 18. 00:06:39
Q18 - What is Loss Function and what are various Loss functions used in DL?
Урок 19. 00:03:41
Q19 - What is Cross Entropy loss function and how is it called in industry?
Урок 20. 00:03:40
Q20 - Why is Cross-entropy preferred as cost function for multi-class classification?
Урок 21. 00:06:11
Q21 - What is SGD and why it’s used in training Neural Networks?
Урок 22. 00:05:52
Q22 - Why does stochastic gradient descent oscillate towards local minima?
Урок 23. 00:05:19
Q23: How is GD different from SGD
Урок 24. 00:06:04
Q24: What is SGD with Momentum
Урок 25. 00:05:27
Q25 - Batch Gradient Descent, Minibatch Gradient Descent vs SGD
Урок 26. 00:06:49
Q26: What is impact of Batch Size
Урок 27. 00:04:10
Q27: Batch Size vs Model Performance
Урок 28. 00:04:39
Q28: What is Hessian, usage in DL
Урок 29. 00:05:29
Q29: What is RMSProp and how does it work?
Урок 30. 00:04:33
Q30: What is Adaptive Learning
Урок 31. 00:07:03
Q31: What is Adam Optimizer
Урок 32. 00:04:53
Q32: What is AdamW Algorithm in Neural Networks
Урок 33. 00:08:32
Q33: What is Batch Normalization
Урок 34. 00:03:39
Q34: What is Layer Normalization
Урок 35. 00:09:23
Q35: What are Residual Connections
Урок 36. 00:03:41
Q36: What is Gradient Clipping
Урок 37. 00:04:05
Q37: What is Xavier Initialization
Урок 38. 00:03:16
Q38: What are ways to solve Vanishing Gradients
Урок 39. 00:01:12
Q39: How to solve Exploding Gradient Problem
Урок 40. 00:02:39
Q40: What is Overfitting
Урок 41. 00:05:19
Q41: What is Dropout
Урок 42. 00:00:42
Q42: How does Dropout prevent Overfitting in Neural Networks
Урок 43. 00:04:42
Q43: Is Dropout like Random Forest
Урок 44. 00:02:36
Q44: What is the impact of DropOut on the training vs testing
Урок 45. 00:03:19
Q45: What are L2 and L1 Regularizations for Overfitting NN
Урок 46. 00:04:05
Q46: What is the difference between L1 and L2 Regularisations
Урок 47. 00:01:52
Q47: How do L1 vs L2 Regularization impact the Weights in a NN?
Урок 48. 00:02:28
Q48: What is the Curse of Dimensionality in Machine Learning | Deep Learning Interview Question
Урок 49. 00:04:05
Q49 - How Deep Learning models tackle the Curse of Dimensionality | Deep Learning Interview Question
Урок 50. 00:02:58
Q50: What are Generative Models, give examples?
Урок 51. 00:03:04
Q51 - What are Discriminative Models, give examples?
Урок 52. 00:08:35
Q52 - What is the difference between generative and discriminative models?
Урок 53. 00:04:31
Q53 - What are Autoencoders and How Do They Work?
Урок 54. 00:04:32
Q54: What is the Difference Beetween Autoenconders and other Neural Networks?
Урок 55. 00:01:25
Q55 - What are some popular autoencoders, mention few?
Урок 56. 00:01:04
Q56 - What is the role of the Loss function in Autoencoders, & how is it different from other NN?
Урок 57. 00:02:21
Q57 - How do autoencoders differ from (PCA)?
Урок 58. 00:03:27
Q58 - Which one is better for reconstruction linear autoencoder or PCA?
Урок 59. 00:06:31
Q59 - How can you recreate PCA with neural networks?
Урок 60. 00:10:36
Q60 - Can You Explain How Autoencoders Can be Used for Anomaly Detection?
Урок 61. 00:02:20
Q61 - What are some applications of AutoEncoders
Урок 62. 00:04:09
Q62 - How can uncertainty be introduced into Autoencoders, & what are the benefits and challenges of doing so?
Урок 63. 00:03:18
Q63 - Can you explain what VAE is and describe its training process?
Урок 64. 00:03:48
Q64 - Explain what Kullback-Leibler (KL) divergence is & why does it matter in VAEs?
Урок 65. 00:01:02
Q65 - Can you explain what reconstruction loss is & it’s function in VAEs?
Урок 66. 00:04:35
Q66 - What is ELBO & What is this trade-off between reconstruction quality & regularization?
Урок 67. 00:03:49
Q67 - Can you explain the training & optimization process of VAEs?
Урок 68. 00:03:12
Q68 - How would you balance reconstruction quality and latent space regularization in a practical Variational Autoencoder implementation?
Урок 69. 00:04:15
Q69 - What is Reparametrization trick and why is it important?
Урок 70. 00:01:45
Q70 - What is DGG "Deep Clustering via a Gaussian-mixture Variational Autoencoder (VAE)” with Graph Embedding
Урок 71. 00:02:24
Q71 - How does a neural network with one layer and one input and output compare to a logistic regression?
Урок 72. 00:01:06
Q72 - In a logistic regression model, will all the gradient descent algorithms lead to the same model if run for a long time?
Урок 73. 00:05:10
Q73 - What is a Convolutional Neural Network?
Урок 74. 00:02:02
Q74 - What is padding and why it’s used in Convolutional Neural Networks (CNNs)?
Урок 75. 00:13:18
Q75 - Padded Convolutions: What are Valid and Same Paddings?
Урок 76. 00:05:43
Q76 - What is stride in CNN and why is it used?
Урок 77. 00:02:28
Q77 - What is the impact of Stride size on CNNs?
Урок 78. 00:09:12
Q78 - What is Pooling, what is the intuition behind it and why is it used in CNNs?
Урок 79. 00:02:49
Q79 - What are common types of pooling in CNN?
Урок 80. 00:03:47
Q80 - Why min pooling is not used?
Урок 81. 00:01:36
Q81 - What is translation invariance and why is it important?
Урок 82. 00:02:55
Q82 - How does a 1D Convolutional Neural Network (CNN) work?
Урок 83. 00:07:09
Q83 - What are Recurrent Neural Networks, and walk me through the architecture of RNNs.
Урок 84. 00:01:30
Q84 - What are the main disadvantages of RNNs, especially in Machine Translation Tasks?
Урок 85. 00:06:16
Q85 - What are some applications of RNN?
Урок 86. 00:05:05
Q86 - What technique is commonly used in RNNs to combat the Vanishing Gradient Problem?
Урок 87. 00:05:24
Q87 - What are LSTMs and their key components?
Урок 88. 00:06:16
Q88 - What limitations of RNN that LSTMs do and don’t address and how?
Урок 89. 00:03:35
Q89 - What is a gated recurrent unit (GRU) and how is it different from LSTMs?
Урок 90. 00:06:17
Q90 - Describe how Generative Adversarial Networks (GANs) work and the roles of the generator and discriminator in learning.
Урок 91. 00:04:10
Q91 - Describe how would you use GANs for image translation or creating photorealistic images?
Урок 92. 00:04:12
Q92 - How would you address mode collapse and vanishing gradients in GAN training, and what is their impact on data quality?
Урок 93. 00:09:04
Q93- Minimax and Nash Equilibrium in GAN
Урок 94. 00:06:01
Q94 - What are token embeddings and what is their function?
Урок 95. 00:11:26
Q95 - What is self-attention mechanism?
Урок 96. 00:06:54
Q96 - What is Multi-Head Self-Attention and how does it enable more effective processing of sequences in Transformers?
Урок 97. 00:05:52
Q97 - What are transformers and why are they important in combating problems of models like RNN and LSTMs?
Урок 98. 00:08:56
Q98 - Walk me through the architecture of transformers.
Урок 99. 00:05:48
Q99 - What are positional encodings and how are they calculated?
Урок 100. 00:02:13
Q100 - Why do we add positional encodings to Transformers but not to
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