#Deep_Learning

Last updated    - V4 (13-Sep-2025)

Level Topic Subtopics Basic Introduction to Deep Learning What is Deep Learning, Difference between ML and DL, History of DL, Applications, Types of Neural Networks Neural Network Fundamentals Perceptron, Neurons, Layers, Activation Functions (Sigmoid, ReLU, Tanh), Forward Propagation Loss Functions & Optimization Mean Squared Error, Cross-Entropy, Gradient Descent, Learning Rate, Optimizers Overview Tools & Frameworks Python, TensorFlow, Keras, PyTorch, Jupyter Notebook, Google Colab Ethics & Safety Bias in DL models, Responsible AI, Model Interpretability, Privacy Concerns, Fairness Intermediate Feedforward & Convolutional Networks Multi-Layer Perceptron (MLP), Forward & Backpropagation, CNN Architecture, Convolution & Pooling, Image Classification Recurrent Neural Networks RNN, LSTM, GRU, Sequence Modeling, Time Series Forecasting, Text Generation Regularization Techniques Dropout, L2/L1 Regularization, Batch Normalization, Early Stopping, Data Augmentation Model Evaluation Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC-AUC, Loss Curves Transfer Learning Pretrained Models, Feature Extraction, Fine-Tuning, Applications in CV & NLP Advanced Advanced Architectures GANs, Variational Autoencoders (VAE), Attention Mechanism, Transformers, Residual Networks (ResNet) Natural Language Processing Tokenization, Embeddings, Word2Vec, GloVe, BERT, GPT, Sequence-to-Sequence Models Computer Vision Advanced Object Detection, Image Segmentation, Instance Segmentation, Attention in CV, Vision Transformers Optimization & Training Advanced Optimizers (Adam, RMSProp), Learning Rate Scheduling, Gradient Clipping, Mixed Precision Training Multi-Modal Learning Text-to-Image, Text-to-Audio, Cross-Modal Representations, Multi-Modal Transformers, Fusion Techniques Expert Reinforcement Learning Markov Decision Processes, Q-Learning, Policy Gradient Methods, Actor-Critic, Multi-Agent RL Generative Deep Learning Advanced GANs (StyleGAN, CycleGAN, BigGAN), Diffusion Models, Generative Transformers, Latent Space Manipulation Explainable & Interpretable DL SHAP, LIME, Counterfactual Analysis, Attention Visualization, Understanding Latent Representations Model Deployment & MLOps Serving Models, APIs, Cloud Deployment, Model Monitoring, CI/CD for DL, Model Versioning Research & Emerging Trends Self-Supervised Learning, Few-Shot & Zero-Shot Learning, Foundation Models, AI Alignment, Responsible Deployment

1. Deep Learning Basics

1. What is Deep Learning?
2. Difference between Machine Learning and Deep Learning.
3. What are artificial neural networks (ANN)?
4. Explain the perceptron model.
5. What are neurons and layers in neural networks?
6. Explain activation functions: Sigmoid, ReLU, Tanh.
7. What is forward propagation?
8. What is backpropagation?
9. Explain the concept of loss functions.
10. What are common loss functions: MSE, Cross-Entropy?
11. Explain gradient descent.
12. What is learning rate and its importance?
13. What are optimizers in deep learning?
14. Difference between batch, stochastic, and mini-batch gradient descent.
15. What is overfitting in deep learning?
16. What is underfitting in deep learning?
17. How do you prevent overfitting?
18. Explain dropout regularization.
19. What is batch normalization?
20. What is data augmentation?
21. Explain train-validation-test split.
22. What are epochs and iterations?
23. What are common datasets for deep learning?
24. Explain supervised vs unsupervised deep learning.
25. What are practical applications of deep learning?

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2. Feedforward & Convolutional Networks

1. What is a feedforward neural network (FNN)?
2. Difference between FNN and RNN.
3. Explain convolutional neural networks (CNN).
4. What is a convolution layer?
5. Explain pooling layers: max pooling, average pooling.
6. How do you apply padding and stride in CNN?
7. Explain fully connected layers in CNN.
8. How do CNNs work for image classification?
9. Explain feature maps in CNN.
10. What is transfer learning?
11. Difference between feature extraction and fine-tuning.
12. Explain common CNN architectures: LeNet, AlexNet, VGG, ResNet.
13. What is residual connection in ResNet?
14. How do you prevent overfitting in CNNs?
15. Explain regularization techniques in CNNs.
16. What are common CNN applications?
17. Explain object detection using CNNs.
18. Explain image segmentation using CNNs.
19. What is instance segmentation?
20. How do you implement CNN in TensorFlow/PyTorch?
21. What is activation map visualization?
22. How do you handle class imbalance in image datasets?
23. Explain image augmentation techniques.
24. How do you optimize CNN training?
25. Explain evaluation metrics for image classification.

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3. Recurrent & Sequence Models

1. What are recurrent neural networks (RNN)?
2. Difference between RNN and CNN.
3. Explain vanishing and exploding gradient problem in RNN.
4. What is Long Short-Term Memory (LSTM)?
5. Explain Gated Recurrent Unit (GRU).
6. Difference between LSTM and GRU.
7. What are sequence-to-sequence models?
8. Explain attention mechanism.
9. What is encoder-decoder architecture?
10. Explain machine translation using RNNs.
11. How do you perform text summarization?
12. How do you perform sentiment analysis with RNNs?
13. Explain time series forecasting using RNNs.
14. How do you handle long sequences in RNN?
15. How do you prevent overfitting in RNNs?
16. Explain teacher forcing in sequence models.
17. What are bidirectional RNNs?
18. How do you implement attention in RNNs?
19. Explain evaluation metrics for sequence models.
20. Difference between sequence classification and sequence generation.
21. How do you visualize RNN outputs?
22. Explain embedding layers in sequence models.
23. How do you handle variable-length sequences?
24. Explain hierarchical RNNs.
25. How do you optimize RNN training performance?

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4. Advanced Deep Learning

1. Explain Generative Adversarial Networks (GANs).
2. What are the components of GAN: generator and discriminator?
3. Explain Variational Autoencoders (VAE).
4. What is latent space in VAEs?
5. Explain Transformer architecture.
6. Difference between Transformer and RNN.
7. Explain self-attention mechanism.
8. What is multi-head attention?
9. Explain positional encoding in Transformers.
10. Explain BERT architecture.
11. Explain GPT architecture.
12. Difference between encoder, decoder, and encoder-decoder models.
13. Explain fine-tuning pre-trained Transformer models.
14. What are foundation models?
15. Explain sequence-to-sequence modeling using Transformers.
16. Explain cross-modal learning (text-to-image, text-to-audio).
17. How do you implement transfer learning in Transformers?
18. Explain reinforcement learning in deep learning.
19. How do you perform hyperparameter tuning for deep learning models?
20. Explain mixed precision training.
21. How do you handle memory optimization in large models?
22. Explain explainable AI techniques in deep learning.
23. How do you monitor deep learning model performance?
24. Explain deployment strategies for deep learning models.
25. What are emerging trends in deep learning research?