6.83 GB | 13min 42s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
FileName :1 Q1 - What is Deep Learning.mp4 | Size: (58.75 MB)
FileName :2 Q2 - How does Deep Learning differ from traditional Machine Learning.mp4 | Size: (60.31 MB)
FileName :3 Q3 - What is a Neural Network.mp4 | Size: (131.26 MB)
FileName :4 Q4 - Explain the concept of a neuron in Deep Learning.mp4 | Size: (49.41 MB)
FileName :5 Q5 - Explain architecture of Neural Networks in simple way.mp4 | Size: (102.18 MB)
FileName :6 Q6 - What is an activation function in a Neural Network.mp4 | Size: (58.81 MB)
FileName :7 Q7 - Name few popular activation functions and describe them.mp4 | Size: (191.39 MB)
FileName :8 Q8 - What happens if you do not use any activation functions in a NN.mp4 | Size: (15.77 MB)
FileName :9 Q9 - Describe how training of basic Neural Networks works.mp4 | Size: (81.86 MB)
FileName :10 Q10 - What is Gradient Descent.mp4 | Size: (185.24 MB)
FileName :11 Q11 - What is the function of an optimizer in Deep Learning.mp4 | Size: (112.76 MB)
FileName :12 Q12 - What is backpropagation, and why is it important in Deep Learning.mp4 | Size: (115.62 MB)
FileName :13 Q13 - How is backpropagation different from gradient descent.mp4 | Size: (48.99 MB)
FileName :14 Q14 - Describe what Vanishing Gradient Problem is and it's impact on NN.mp4 | Size: (98.59 MB)
FileName :15 Q15 - Describe what Exploding Gradients Problem is and it's impact on NN.mp4 | Size: (106.18 MB)
FileName :16 Q16 - There is a neuron results in a large error in backpropagation Reason.mp4 | Size: (62.44 MB)
FileName :17 Q17 - What do you understand by a computational graph.mp4 | Size: (88.01 MB)
FileName :18 Q18 - What is Loss Function and what are various Loss functions used in DL.mp4 | Size: (80.72 MB)
FileName :19 Q19 - What is Cross Entropy loss function and how is it called in industry.mp4 | Size: (49.37 MB)
FileName :20 Q20 - Why is Cross-entropy favored in multi-class classification.mp4 | Size: (53.79 MB)
FileName :21 Q21 - What is SGD and why it's used in training Neural Networks.mp4 | Size: (109.68 MB)
FileName :22 Q22 - Why does stochastic gradient descent oscillate towards local minima.mp4 | Size: (95 MB)
FileName :23 Q23 - How is GD different from SGD.mp4 | Size: (85.02 MB)
FileName :24 Q24 - How can optimization methods like GD be improved.mp4 | Size: (97.91 MB)
FileName :25 Q25 - Compare batch GD, minibatch GD, and SGD.mp4 | Size: (74.76 MB)
FileName :26 Q26 - How to decide batch size in deep learning.mp4 | Size: (58.54 MB)
FileName :27 Q27 - How does the batch size impact the performance of a deep learning model.mp4 | Size: (65.45 MB)
FileName :28 Q28 - What is Hessian, and how can it be used for faster training.mp4 | Size: (91.58 MB)
FileName :29 Q29 - What is RMSProp and how does it work.mp4 | Size: (103.49 MB)
FileName :30 Q30 - Discuss the concept of an adaptive learning rate.mp4 | Size: (70.19 MB)
FileName :31 Q31 - What is Adam and why is it used most of the time in NNs.mp4 | Size: (91.91 MB)
FileName :32 Q32 - What is AdamW and why it's preferred over Adam.mp4 | Size: (78.67 MB)
FileName :33 Q33 - What is Batch Normalization and why it's used in NN.mp4 | Size: (154.4 MB)
FileName :34 Q34 - What is Layer Normalization, and why it's used in NN.mp4 | Size: (52.29 MB)
FileName :35 Q35 - What are Residual Connections and their function in NN.mp4 | Size: (151.46 MB)
FileName :36 Q36 - What is Gradient clipping and their impact on NN.mp4 | Size: (57.89 MB)
FileName :37 Q37 - What is Xavier Initialization and why it's used in NN.mp4 | Size: (76.7 MB)
FileName :38 Q38 - What are different ways to solve Vanishing gradients.mp4 | Size: (59.65 MB)
FileName :39 Q39 - What are ways to solve Exploding Gradients.mp4 | Size: (24.97 MB)
FileName :40 Q40 - What's the impact of overfitting in neural networks with large weights.mp4 | Size: (46.05 MB)
FileName :41 Q41 - What is Dropout and how does it work.mp4 | Size: (83.69 MB)
FileName :42 Q42 - How does Dropout prevent overfitting in NN.mp4 | Size: (18.5 MB)
FileName :43 Q43 - Is Dropout like Random Forest.mp4 | Size: (72.07 MB)
FileName :44 Q44 - What is the impact of Drop Out on the training vs testing.mp4 | Size: (32.06 MB)
FileName :45 Q45 - What are L2L1 Regularizations and how do they prevent overfitting in NN.mp4 | Size: (53.53 MB)
FileName :46 Q46 - What is the difference between L1 and L2 regularizations in NN.mp4 | Size: (62.33 MB)
FileName :47 Q47 - How do L1 vs L2 Regularization impact the Weights in a NN.mp4 | Size: (33.31 MB)
FileName :48 Q48 - What is the curse of dimensionality in ML or AI.mp4 | Size: (28.17 MB)
FileName :49 Q49 - How deep learning models tackle the curse of dimensionality.mp4 | Size: (65.81 MB)
FileName :50 Q50 - What are Generative Models, give examples.mp4 | Size: (57.96 MB)
FileName :51 Q51 - What are Discriminative Models, give examples.mp4 | Size: (56.78 MB)
FileName :52 Q52 - What is the difference between generative and discriminative models.mp4 | Size: (122.07 MB)
FileName :53 Q53 - What are Autoencoders and How Do They Work.mp4 | Size: (83.03 MB)
FileName :54 Q54 - What is the Difference Between Autoencoders and Other Neural Networks.mp4 | Size: (83.24 MB)
FileName :55 Q55 - What are some popular autoencoders, mention few.mp4 | Size: (13.85 MB)
FileName :56 Q56 - What is the role of the Loss function in Autoencoders.mp4 | Size: (15.2 MB)
FileName :57 Q57 - How do autoencoders differ from (PCA).mp4 | Size: (39.63 MB)
FileName :58 Q58 - Which one is better for reconstruction linear autoencoder or PCA.mp4 | Size: (65.03 MB)
FileName :59 Q59 - How can you recreate PCA with neural networks.mp4 | Size: (67.04 MB)
FileName :60 Q60 - Can You Explain How Autoencoders Can be Used for Anomaly Detection.mp4 | Size: (108.64 MB)
FileName :61 Q61 - What are some applications of AutoEncoders.mp4 | Size: (34.22 MB)
FileName :62 Q62 - How can uncertainty be introduced into Autoencoders.mp4 | Size: (62.56 MB)
FileName :63 Q63 - Can you explain what VAE is and describe its training process.mp4 | Size: (57.01 MB)
FileName :64 Q64 - Explain what Kullback-Leibler (KL) divergence is.mp4 | Size: (70.39 MB)
FileName :65 Q65 - Can you explain what reconstruction loss is & it's function in VAEs.mp4 | Size: (18.74 MB)
FileName :66 Q66 - What is ELBO & What is this trade-off between reconstruction quality.mp4 | Size: (83.8 MB)
FileName :67 Q67 - Can you explain the training & optimization process of VAEs.mp4 | Size: (55.14 MB)
FileName :68 Q68 - Balancing VAE reconstruction and latent space.mp4 | Size: (53.22 MB)
FileName :69 Q69 - What is Reparametrization trick and why is it important.mp4 | Size: (59.63 MB)
FileName :70 Q70 - What is DGG Deep Clustering with Gaussian-mixture VAE and Graph Embedding.mp4 | Size: (24.17 MB)
FileName :71 Q71 - Neural net vs logistic regression comparison.mp4 | Size: (32.44 MB)
FileName :72 Q72 - Do all gradients converge in logistic regression.mp4 | Size: (15.49 MB)
FileName :73 Q73 - What is a Convolutional Neural Network.mp4 | Size: (107.32 MB)
FileName :74 Q74 - What is padding and why it's used in Convolutional Neural Networks (CNNs).mp4 | Size: (28.91 MB)
FileName :75 Q75 - Padded Convolutions What are Valid and Same Paddings.mp4 | Size: (77.27 MB)
FileName :76 Q76 - What is stride in CNN and why is it used.mp4 | Size: (51.48 MB)
FileName :77 Q77 - What is the impact of Stride size on CNNs.mp4 | Size: (31.08 MB)
FileName :78 Q78 - What is Pooling, what is the intuition behind it and why is it used in CNN.mp4 | Size: (84.05 MB)
FileName :79 Q79 - What are common types of pooling in CNN.mp4 | Size: (39.17 MB)
FileName :80 Q80 - Why min pooling is not used.mp4 | Size: (52.57 MB)
FileName :81 Q81 - What is translation invariance and why is it important.mp4 | Size: (22.97 MB)
FileName :82 Q82 - How does a 1D Convolutional Neural Network (CNN) work.mp4 | Size: (41.05 MB)
FileName :83 Q83 - What are Recurrent Neural Networks.mp4 | Size: (95.82 MB)
FileName :84 Q84 - What are the main disadvantages of RNNs.mp4 | Size: (20.52 MB)
FileName :85 Q85 - What are some applications of RNN.mp4 | Size: (53.95 MB)
FileName :86 Q86 - How do you fix Vanishing Gradient in RNNs.mp4 | Size: (43.31 MB)
FileName :87 Q87 - What are LSTMs and their key components.mp4 | Size: (36.53 MB)
FileName :88 Q88 - What limitations of RNN that LSTMs do and don't address and how.mp4 | Size: (54.68 MB)
FileName :89 Q89 - What is a gated recurrent unit (GRU) and how is it different from LSTMs.mp4 | Size: (35.29 MB)
FileName :90 Q90 - How do GANs and their components work.mp4 | Size: (93.55 MB)
FileName :91 Q91 - Describe how you would use GANs for image translation.mp4 | Size: (81.06 MB)
FileName :92 Q92 - How would you address mode collapse and vanishing gradients in GAN.mp4 | Size: (80.85 MB)
FileName :93 Q94 - What are token embeddings and what is their function.mp4 | Size: (60.91 MB)
FileName :94 Q95 - What is the self-attention mechanism.mp4 | Size: (181.73 MB)
FileName :95 Q96 - What is Multi-Head Self-Attention.mp4 | Size: (93.89 MB)
FileName :96 Q97 - Transformers vs RNNLSTM limitations.mp4 | Size: (95.72 MB)
FileName :97 Q98 - Walk me through the architecture of transformers.mp4 | Size: (159.86 MB)
FileName :98 Q99 - What are positional encodings and how are they calculated.mp4 | Size: (98.56 MB)
FileName :99 Q100 - Purpose of positional encodings in Transformers.mp4 | Size: (41.74 MB)]
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