What is Deep Learning?, Difference between Machine Learning and Deep Learning, Why Deep Learning is effective for large datasets

Self-driving cars (Object detection), Voice assistants (Speech recognition), Generative AI (Text, image, and music generation)

Neural Network architecture (Input, Hidden, and Output layers), Forward propagation and backpropagation, Weight optimization using loss functions

Weighted sum of inputs, Activation functions (ReLU, Sigmoid, Tanh), Error minimization through backpropagation

ReLU (Rectified Linear Unit), Sigmoid, Tanh, Softmax (for multi-class classification)

Mean Squared Error (MSE) – Regression, Cross-Entropy Loss – Classification

Gradient Descent, Adam Optimizer

Learning rate adjustments, Epochs and batch size optimization

Structure with unidirectional data flow, Used for structured data tasks (regression, classification)

Specialized for image processing, Uses filters and pooling layers, Applications: Image classification, object detection

Designed for sequential data (time series, text), Memory cells retain previous information, Applications: Language models, speech recognition

State-of-the-art architecture for NLP and Gen AI, Self-attention mechanism for context understanding, Applications: ChatGPT, BERT, DALL·E

CNNs for image classification and face detection, Applications: Medical imaging, autonomous vehicles

RNNs and Transformers for text generation and translation, Applications: Chatbots, sentiment analysis

GANs and Diffusion Models for content generation, Applications: Deepfakes, AI-generated art

Deep learning for self-driving cars (object detection), AI-powered robotics and drones