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

Name: Deep Learning Interview Prep Course | Full Course [100 Q&A's]
Price: 9 USD
Availability: InStock

8h 12m 34s

English

Paid

December 20, 2024

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.

Watch Online

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

0:00

#1: Q1 - What is Deep Learning?

All Course Lessons (100)

#	Lesson Title	Duration
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 itвЂ™s impact on NN	07:01
15	Q15 - Describe what Exploding Gradients Problem is and itвЂ™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 itвЂ™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 & itвЂ™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)вЂќ 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 itвЂ™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 donвЂ™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

Unlock unlimited learning

Get instant access to all 99 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.

Learn more about subscription

Comments

0 comments

Want to join the conversation?

Similar courses

InfraExpert

Sources: algoexpert

Infrastructure engineering is built on extensive foundations in networking, operating systems, virtualization, databases, and cloud computing. Our crash course is intelligently...

12 hours 1 minute 24 seconds

Frontend System Design

Sources: LearnersBucket | Prashant Yadav

Become the Alpha frontend engineer by designing 30+ distinct web applications and all the fundamentals. Learn the advanced concepts, strategies, techniques, and

The Coding Interview Bootcamp: Algorithms + Data Structures

Sources: udemy, Stephen Grider

Data Structures? They're here. Algorithms? Covered. Lots of questions with well-explained solutions? Yep! If you're nervous about your first coding interview, or anxious abo...

12 hours 52 minutes 17 seconds

50 Frontend Interview Questions

Sources: algoexpert

The leading platform to prepare for frontend interviews. Master the fundamentals of HTML, CSS, and JavaScript, sharpen your frontend skills, and land your dream

12 hours 37 minutes 17 seconds

[2021] Career Hacking™: Resume, LinkedIn, Interviewing +More

Sources: udemy

Welcome to the world's BESTSELLING career management course from Eazl and former Bay Area recruiter, Davis Jones! This course is tested by the market and has be

5 hours 34 minutes 30 seconds