UDEMY Deep Learning Interview Preparation Course 100 Q and As BOOKWARE-iLEARN

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6.9G	Chapter_1-Deep_Learning_Interview_Preparation
59M	Chapter_1-Deep_Learning_Interview_Preparation/1 Q1 - What is Deep Learning.mp4
186M	Chapter_1-Deep_Learning_Interview_Preparation/10 Q10 - What is Gradient Descent.mp4
113M	Chapter_1-Deep_Learning_Interview_Preparation/11 Q11 - What is the function of an optimizer in Deep Learning.mp4
116M	Chapter_1-Deep_Learning_Interview_Preparation/12 Q12 - What is backpropagation, and why is it important in Deep Learning.mp4
49M	Chapter_1-Deep_Learning_Interview_Preparation/13 Q13 - How is backpropagation different from gradient descent.mp4
99M	Chapter_1-Deep_Learning_Interview_Preparation/14 Q14 - Describe what Vanishing Gradient Problem is and it’s impact on NN.mp4
107M	Chapter_1-Deep_Learning_Interview_Preparation/15 Q15 - Describe what Exploding Gradients Problem is and it’s impact on NN.mp4
63M	Chapter_1-Deep_Learning_Interview_Preparation/16 Q16 - There is a neuron results in a large error in backpropagation Reason.mp4
89M	Chapter_1-Deep_Learning_Interview_Preparation/17 Q17 - What do you understand by a computational graph.mp4
81M	Chapter_1-Deep_Learning_Interview_Preparation/18 Q18 - What is Loss Function and what are various Loss functions used in DL.mp4
50M	Chapter_1-Deep_Learning_Interview_Preparation/19 Q19 - What is Cross Entropy loss function and how is it called in industry.mp4
61M	Chapter_1-Deep_Learning_Interview_Preparation/2 Q2 - How does Deep Learning differ from traditional Machine Learning.mp4
54M	Chapter_1-Deep_Learning_Interview_Preparation/20 Q20 - Why is Cross-entropy favored in multi-class classification.mp4
110M	Chapter_1-Deep_Learning_Interview_Preparation/21 Q21 - What is SGD and why it’s used in training Neural Networks.mp4
96M	Chapter_1-Deep_Learning_Interview_Preparation/22 Q22 - Why does stochastic gradient descent oscillate towards local minima.mp4
86M	Chapter_1-Deep_Learning_Interview_Preparation/23 Q23 - How is GD different from SGD.mp4
98M	Chapter_1-Deep_Learning_Interview_Preparation/24 Q24 - How can optimization methods like GD be improved.mp4
75M	Chapter_1-Deep_Learning_Interview_Preparation/25 Q25 - Compare batch GD, minibatch GD, and SGD.mp4
59M	Chapter_1-Deep_Learning_Interview_Preparation/26 Q26 - How to decide batch size in deep learning.mp4
66M	Chapter_1-Deep_Learning_Interview_Preparation/27 Q27 - How does the batch size impact the performance of a deep learning model.mp4
92M	Chapter_1-Deep_Learning_Interview_Preparation/28 Q28 - What is Hessian, and how can it be used for faster training.mp4
104M	Chapter_1-Deep_Learning_Interview_Preparation/29 Q29 - What is RMSProp and how does it work.mp4
132M	Chapter_1-Deep_Learning_Interview_Preparation/3 Q3 - What is a Neural Network.mp4
71M	Chapter_1-Deep_Learning_Interview_Preparation/30 Q30 - Discuss the concept of an adaptive learning rate.mp4
92M	Chapter_1-Deep_Learning_Interview_Preparation/31 Q31 - What is Adam and why is it used most of the time in NNs.mp4
79M	Chapter_1-Deep_Learning_Interview_Preparation/32 Q32 - What is AdamW and why it’s preferred over Adam.mp4
155M	Chapter_1-Deep_Learning_Interview_Preparation/33 Q33 - What is Batch Normalization and why it’s used in NN.mp4
53M	Chapter_1-Deep_Learning_Interview_Preparation/34 Q34 - What is Layer Normalization, and why it’s used in NN.mp4
152M	Chapter_1-Deep_Learning_Interview_Preparation/35 Q35 - What are Residual Connections and their function in NN.mp4
58M	Chapter_1-Deep_Learning_Interview_Preparation/36 Q36 - What is Gradient clipping and their impact on NN.mp4
77M	Chapter_1-Deep_Learning_Interview_Preparation/37 Q37 - What is Xavier Initialization and why it’s used in NN.mp4
60M	Chapter_1-Deep_Learning_Interview_Preparation/38 Q38 - What are different ways to solve Vanishing gradients.mp4
25M	Chapter_1-Deep_Learning_Interview_Preparation/39 Q39 - What are ways to solve Exploding Gradients.mp4
50M	Chapter_1-Deep_Learning_Interview_Preparation/4 Q4 - Explain the concept of a neuron in Deep Learning.mp4
47M	Chapter_1-Deep_Learning_Interview_Preparation/40 Q40 - What's the impact of overfitting in neural networks with large weights.mp4
84M	Chapter_1-Deep_Learning_Interview_Preparation/41 Q41 - What is Dropout and how does it work.mp4
19M	Chapter_1-Deep_Learning_Interview_Preparation/42 Q42 - How does Dropout prevent overfitting in NN.mp4
73M	Chapter_1-Deep_Learning_Interview_Preparation/43 Q43 - Is Dropout like Random Forest.mp4
33M	Chapter_1-Deep_Learning_Interview_Preparation/44 Q44 - What is the impact of Drop Out on the training vs testing.mp4
54M	Chapter_1-Deep_Learning_Interview_Preparation/45 Q45 - What are L2L1 Regularizations and how do they prevent overfitting in NN.mp4
63M	Chapter_1-Deep_Learning_Interview_Preparation/46 Q46 - What is the difference between L1 and L2 regularizations in NN.mp4
34M	Chapter_1-Deep_Learning_Interview_Preparation/47 Q47 - How do L1 vs L2 Regularization impact the Weights in a NN.mp4
29M	Chapter_1-Deep_Learning_Interview_Preparation/48 Q48 - What is the curse of dimensionality in ML or AI.mp4
66M	Chapter_1-Deep_Learning_Interview_Preparation/49 Q49 - How deep learning models tackle the curse of dimensionality.mp4
103M	Chapter_1-Deep_Learning_Interview_Preparation/5 Q5 - Explain architecture of Neural Networks in simple way.mp4
58M	Chapter_1-Deep_Learning_Interview_Preparation/50 Q50 - What are Generative Models, give examples.mp4
57M	Chapter_1-Deep_Learning_Interview_Preparation/51 Q51 - What are Discriminative Models, give examples.mp4
123M	Chapter_1-Deep_Learning_Interview_Preparation/52 Q52 - What is the difference between generative and discriminative models.mp4
84M	Chapter_1-Deep_Learning_Interview_Preparation/53 Q53 - What are Autoencoders and How Do They Work.mp4
84M	Chapter_1-Deep_Learning_Interview_Preparation/54 Q54 - What is the Difference Between Autoencoders and Other Neural Networks.mp4
14M	Chapter_1-Deep_Learning_Interview_Preparation/55 Q55 - What are some popular autoencoders, mention few.mp4
16M	Chapter_1-Deep_Learning_Interview_Preparation/56 Q56 - What is the role of the Loss function in Autoencoders.mp4
40M	Chapter_1-Deep_Learning_Interview_Preparation/57 Q57 - How do autoencoders differ from (PCA).mp4
66M	Chapter_1-Deep_Learning_Interview_Preparation/58 Q58 - Which one is better for reconstruction linear autoencoder or PCA.mp4
68M	Chapter_1-Deep_Learning_Interview_Preparation/59 Q59 - How can you recreate PCA with neural networks.mp4
59M	Chapter_1-Deep_Learning_Interview_Preparation/6 Q6 - What is an activation function in a Neural Network.mp4
109M	Chapter_1-Deep_Learning_Interview_Preparation/60 Q60 - Can You Explain How Autoencoders Can be Used for Anomaly Detection.mp4
35M	Chapter_1-Deep_Learning_Interview_Preparation/61 Q61 - What are some applications of AutoEncoders.mp4
63M	Chapter_1-Deep_Learning_Interview_Preparation/62 Q62 - How can uncertainty be introduced into Autoencoders.mp4
58M	Chapter_1-Deep_Learning_Interview_Preparation/63 Q63 - Can you explain what VAE is and describe its training process.mp4
71M	Chapter_1-Deep_Learning_Interview_Preparation/64 Q64 - Explain what Kullback-Leibler (KL) divergence is.mp4
19M	Chapter_1-Deep_Learning_Interview_Preparation/65 Q65 - Can you explain what reconstruction loss is & it’s function in VAEs.mp4
84M	Chapter_1-Deep_Learning_Interview_Preparation/66 Q66 - What is ELBO & What is this trade-off between reconstruction quality.mp4
56M	Chapter_1-Deep_Learning_Interview_Preparation/67 Q67 - Can you explain the training & optimization process of VAEs.mp4
54M	Chapter_1-Deep_Learning_Interview_Preparation/68 Q68 - Balancing VAE reconstruction and latent space.mp4
60M	Chapter_1-Deep_Learning_Interview_Preparation/69 Q69 - What is Reparametrization trick and why is it important.mp4
192M	Chapter_1-Deep_Learning_Interview_Preparation/7 Q7 - Name few popular activation functions and describe them.mp4
25M	Chapter_1-Deep_Learning_Interview_Preparation/70 Q70 - What is DGG Deep Clustering with Gaussian-mixture VAE and Graph Embedding.mp4
33M	Chapter_1-Deep_Learning_Interview_Preparation/71 Q71 - Neural net vs logistic regression comparison.mp4
16M	Chapter_1-Deep_Learning_Interview_Preparation/72 Q72 - Do all gradients converge in logistic regression.mp4
108M	Chapter_1-Deep_Learning_Interview_Preparation/73 Q73 - What is a Convolutional Neural Network.mp4
29M	Chapter_1-Deep_Learning_Interview_Preparation/74 Q74 - What is padding and why it’s used in Convolutional Neural Networks (CNNs).mp4
78M	Chapter_1-Deep_Learning_Interview_Preparation/75 Q75 - Padded Convolutions What are Valid and Same Paddings.mp4
52M	Chapter_1-Deep_Learning_Interview_Preparation/76 Q76 - What is stride in CNN and why is it used.mp4
32M	Chapter_1-Deep_Learning_Interview_Preparation/77 Q77 - What is the impact of Stride size on CNNs.mp4
85M	Chapter_1-Deep_Learning_Interview_Preparation/78 Q78 - What is Pooling, what is the intuition behind it and why is it used in CNN.mp4
40M	Chapter_1-Deep_Learning_Interview_Preparation/79 Q79 - What are common types of pooling in CNN.mp4
16M	Chapter_1-Deep_Learning_Interview_Preparation/8 Q8 - What happens if you do not use any activation functions in a NN.mp4
53M	Chapter_1-Deep_Learning_Interview_Preparation/80 Q80 - Why min pooling is not used.mp4
23M	Chapter_1-Deep_Learning_Interview_Preparation/81 Q81 - What is translation invariance and why is it important.mp4
42M	Chapter_1-Deep_Learning_Interview_Preparation/82 Q82 - How does a 1D Convolutional Neural Network (CNN) work.mp4
96M	Chapter_1-Deep_Learning_Interview_Preparation/83 Q83 - What are Recurrent Neural Networks.mp4
21M	Chapter_1-Deep_Learning_Interview_Preparation/84 Q84 - What are the main disadvantages of RNNs.mp4
54M	Chapter_1-Deep_Learning_Interview_Preparation/85 Q85 - What are some applications of RNN.mp4
44M	Chapter_1-Deep_Learning_Interview_Preparation/86 Q86 - How do you fix Vanishing Gradient in RNNs.mp4
37M	Chapter_1-Deep_Learning_Interview_Preparation/87 Q87 - What are LSTMs and their key components.mp4
55M	Chapter_1-Deep_Learning_Interview_Preparation/88 Q88 - What limitations of RNN that LSTMs do and don’t address and how.mp4
36M	Chapter_1-Deep_Learning_Interview_Preparation/89 Q89 - What is a gated recurrent unit (GRU) and how is it different from LSTMs.mp4
82M	Chapter_1-Deep_Learning_Interview_Preparation/9 Q9 - Describe how training of basic Neural Networks works.mp4
94M	Chapter_1-Deep_Learning_Interview_Preparation/90 Q90 -  How do GANs and their components work.mp4
82M	Chapter_1-Deep_Learning_Interview_Preparation/91 Q91 - Describe how you would use GANs for image translation.mp4
81M	Chapter_1-Deep_Learning_Interview_Preparation/92 Q92 -  How would you address mode collapse and vanishing gradients in GAN.mp4
61M	Chapter_1-Deep_Learning_Interview_Preparation/93 Q94 - What are token embeddings and what is their function.mp4
182M	Chapter_1-Deep_Learning_Interview_Preparation/94 Q95 - What is the self-attention mechanism.mp4
94M	Chapter_1-Deep_Learning_Interview_Preparation/95 Q96 - What is Multi-Head Self-Attention.mp4
96M	Chapter_1-Deep_Learning_Interview_Preparation/96 Q97 - Transformers vs RNNLSTM limitations.mp4
160M	Chapter_1-Deep_Learning_Interview_Preparation/97 Q98 - Walk me through the architecture of transformers.mp4
99M	Chapter_1-Deep_Learning_Interview_Preparation/98 Q99 - What are positional encodings and how are they calculated.mp4
42M	Chapter_1-Deep_Learning_Interview_Preparation/99 Q100 - Purpose of positional encodings in Transformers.mp4
6.9G	total