Deep Learning Interview Prep Course | Full Course [100 Q&A's]

8h 12m 34s
English
Paid

Course description

Attend a deep learning interview with confidence using our comprehensive preparatory course! Master key concepts and advanced techniques such as GAN and Transformers. Deepen your knowledge and boost your confidence with detailed answers to the most popular interview questions.

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

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