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

8h 12m 34s
English
Paid

Deep Learning Interview Prep Course | Full Course [100 Q&A's] is a 100-lesson 8 hours 12 minutes self-paced course by LunarTech. Attend a deep learning interview with confidence using our comprehensive preparatory course!

Course facts

Lessons
100
Duration
8 hours 12 minutes
Level
All levels
Language
English
Updated
Instructor
LunarTech
Price
Premium
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.

Who teaches Deep Learning Interview Prep Course | Full Course [100 Q&A's]? LunarTech

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LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.

The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.

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#1: Q1 - What is Deep Learning?
All Course Lessons (100)
#Lesson TitleDurationAccess
1
Q1 - What is Deep Learning? Demo
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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Frequently asked questions

What prerequisites should I have before enrolling in this course?
Before enrolling in this deep learning interview prep course, you should have a foundational understanding of machine learning concepts. Familiarity with basic neural network architecture, activation functions, and optimization techniques such as gradient descent and backpropagation will be beneficial. While the course is designed to cover these topics, a prior exposure will help you grasp advanced topics more effectively.
What are some key techniques covered in the course?
The course covers a wide range of deep learning techniques essential for interviews, including Generative Adversarial Networks (GANs) and Transformers. It also delves into optimization methods like SGD, Adam Optimizer, and RMSProp. Additionally, the course explains critical concepts such as the Vanishing Gradient Problem, Overfitting, and techniques to address these issues, like Dropout and Regularization.
Is this course suitable for someone looking to transition into a deep learning role?
Yes, the course is designed for individuals preparing for deep learning interviews, making it suitable for those transitioning into a deep learning role. It provides a comprehensive overview of essential deep learning concepts and techniques, enabling students to discuss them confidently during interviews. The lessons target common interview questions, helping learners understand what to expect in a professional setting.
How does the depth of this course compare to other deep learning courses?
This course uniquely focuses on preparing students for deep learning interviews by covering 100 Q&A sessions that explore theoretical and practical aspects of the field. Unlike other deep learning courses that may emphasize building models or projects, this course centers on understanding and articulating key concepts such as neural network architecture, optimization techniques, and model evaluation, providing a comprehensive preparation for technical interviews.
What deep learning tools and platforms are discussed in the course?
While the course does not focus on specific software tools or platforms, it covers theoretical concepts and techniques applicable across various deep learning frameworks like TensorFlow and PyTorch. Discussions on optimization algorithms like Adam and SGD, and techniques like Gradient Clipping and Batch Normalization are relevant to any deep learning platform, promoting a versatile understanding of the field.
What topics are not covered in this course?
The course focuses specifically on preparing for deep learning interviews and does not delve into hands-on project work, detailed coding exercises, or specific platform tutorials such as TensorFlow or PyTorch. It also does not cover related fields such as computer vision or natural language processing in depth, except where they directly relate to interview preparation through concepts like Transformers.
How can this course benefit my career in deep learning?
This course prepares you for deep learning interviews by reinforcing your understanding of critical concepts and techniques, helping you articulate your knowledge confidently. It covers advanced topics such as GANs and Transformers, which are highly relevant in cutting-edge deep learning roles. Mastery of these concepts can facilitate career advancement in areas like AI research, data science, and machine learning engineering, where deep learning is integral.