09 January 2026

#Deep Learning

#Deep Learning

Key Concepts


S.No Topic Sub-Topics
1 Deep Learning What is deep learning, History, DL vs ML, Applications, Challenges
2 Linear Algebra Refresher Vectors, Matrices, Dot product, Eigenvalues, Matrix operations
3 Probability & Statistics Random variables, Probability distributions, Mean & variance, Bayes theorem, Expectation
4 Optimization Basics Loss functions, Cost functions, Convex vs non-convex, Gradient descent, Learning rate
5 Neural Network Basics Perceptron, Neurons, Weights & bias, Activation functions, Forward propagation
6 Activation Functions Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax
7 Backpropagation Chain rule, Gradient computation, Weight updates, Vanishing gradients, Exploding gradients
8 Training Deep Neural Networks Epochs, Batch size, Initialization, Convergence, Overfitting
9 Regularization Techniques L1, L2, Dropout, Early stopping, Data augmentation
10 Optimizers SGD, Momentum, RMSProp, Adam, AdamW
11 Loss Functions MSE, MAE, Cross-entropy, Hinge loss, KL divergence
12 Deep Learning Frameworks TensorFlow basics, Keras API, PyTorch basics, Autograd, Model training loop
13 Convolutional Neural Networks (CNN) Convolution layers, Pooling, Padding, Stride, Feature maps
14 CNN Architectures LeNet, AlexNet, VGG, ResNet, Inception
15 Image Classification Dataset preparation, Transfer learning, Fine-tuning, Evaluation metrics, Deployment basics
16 Sequence Modeling Time series, Sequential data, Tokenization, Padding, Masking
17 Recurrent Neural Networks (RNN) RNN architecture, BPTT, Vanishing gradients, Use cases, Limitations
18 LSTM & GRU Cell states, Gates, LSTM vs GRU, Applications, Training tips
19 Attention Mechanism Why attention, Self-attention, Encoder-decoder attention, Scaled dot-product, Benefits
20 Transformers Transformer architecture, Positional encoding, Multi-head attention, Encoder-decoder, Training
21 Natural Language Processing with DL Word embeddings, RNN for NLP, Transformers for NLP, Text classification, NER
22 Autoencoders Basic autoencoder, Sparse AE, Denoising AE, Variational AE, Use cases
23 Generative Models GAN basics, Generator & discriminator, Training instability, DCGAN, Applications
24 Advanced CNN Applications Object detection, Image segmentation, Face recognition, Medical imaging, OCR
25 Transfer Learning & Fine-tuning Pretrained models, Layer freezing, Domain adaptation, Benefits, Limitations
26 Model Evaluation Accuracy, Precision, Recall, F1-score, ROC-AUC
27 Hyperparameter Tuning Grid search, Random search, Bayesian optimization, Learning rate schedules, Batch tuning
28 Model Optimization Pruning, Quantization, Knowledge distillation, Mixed precision, Inference optimization
29 Deployment of DL Models REST APIs, Model serving, Cloud deployment, Edge deployment, Monitoring
30 Advanced & Emerging Topics Self-supervised learning, Multimodal models, Foundation models, Ethical AI, Future trends

Interview question

Basic Level

  1. What is Deep Learning?
  2. Difference between Machine Learning and Deep Learning.
  3. What is a Neural Network?
  4. What is a perceptron?
  5. What is an activation function?
  6. What are weights and biases?
  7. What is forward propagation?
  8. What is backpropagation?
  9. What is a loss function?
  10. What is gradient descent?
  11. What is a learning rate?
  12. What are epochs and batches?
  13. What is overfitting?
  14. What is underfitting?
  15. What is regularization?
  16. What is dropout?
  17. What is a convolutional neural network (CNN)?
  18. What is pooling in CNN?
  19. What is a recurrent neural network (RNN)?
  20. What is an LSTM?
  21. What is a GRU?
  22. What is batch normalization?
  23. What is data augmentation?
  24. What is feature extraction?
  25. What is a deep neural network (DNN)?

Intermediate Level

  1. Explain how backpropagation works.
  2. What is the vanishing gradient problem?
  3. What is the exploding gradient problem?
  4. What is weight initialization?
  5. What is Xavier/Glorot initialization?
  6. What is He initialization?
  7. What are optimizers (SGD, Adam, RMSProp)?
  8. What is model capacity?
  9. What is early stopping?
  10. What is cross-entropy loss?
  11. What is mean squared error?
  12. What are CNN kernels/filters?
  13. What is padding and stride?
  14. What is a fully connected layer?
  15. What is transfer learning?
  16. What are embeddings?
  17. What is a sequence-to-sequence (Seq2Seq) model?
  18. What is teacher forcing?
  19. What is attention mechanism?
  20. What is self-attention?
  21. What is multi-head attention?
  22. What is positional encoding?
  23. What is a transformer?
  24. What are residual connections?
  25. What is a skip connection?

Advanced Level

  1. Explain the architecture of a CNN end-to-end.
  2. Explain the architecture of an RNN end-to-end.
  3. Explain the architecture of LSTM and GRU in detail.
  4. What is the receptive field in CNN?
  5. What is dilated convolution?
  6. What is depthwise separable convolution?
  7. What is batch vs layer vs group normalization?
  8. What is label smoothing?
  9. What is attention score calculation?
  10. What is cross-attention?
  11. What are encoder and decoder blocks in transformers?
  12. What is beam search decoding?
  13. What is scheduled sampling?
  14. What is gradient clipping?
  15. What is gradient checkpointing?
  16. Explain vanishing gradient mitigation methods.
  17. What are VAEs (Variational Autoencoders)?
  18. What are GANs (Generative Adversarial Networks)?
  19. What is mode collapse in GANs?
  20. What is contrastive learning?
  21. What is self-supervised learning?
  22. What is metric learning?
  23. What is Siamese network?
  24. What is cosine similarity in embeddings?
  25. What is knowledge distillation?

Expert Level

  1. Explain the transformer architecture at scale.
  2. What are Mixture-of-Experts (MoE) models?
  3. What is sparse attention?
  4. What is FlashAttention?
  5. What is rotary positional embedding (RoPE)?
  6. What is ALiBi (Attention with Linear Biases)?
  7. What is diffusion modeling?
  8. Explain the U-Net architecture used in diffusion.
  9. What is reinforcement learning in deep learning?
  10. What is Deep Q-Learning?
  11. What is policy gradient?
  12. What is PPO (Proximal Policy Optimization)?
  13. What is distributed training?
  14. Difference between data parallelism and model parallelism.
  15. What is pipeline parallelism?
  16. What is tensor parallelism?
  17. What is quantization-aware training?
  18. What is pruning in neural networks?
  19. What is model compression?
  20. What is federated learning?
  21. What is edge deployment for deep learning models?
  22. What are safety and alignment concerns in deep models?
  23. What are adversarial attacks?
  24. What are adversarial defense techniques?
  25. What are current research trends in deep learning?

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