Deep Learning, subset of Machine Learning

Deep Learning (DL) is a subset of Machine Learning (ML) that focuses on using neural networks with multiple layers (hence "deep") to process and learn from large amounts of data. It is inspired by the structure and function of the human brain and is particularly effective for tasks like image recognition, natural language processing, speech recognition, and autonomous systems.

Key Aspects of Deep Learning

1. Neural Networks: DL relies on artificial neural networks (ANNs), especially deep neural networks (DNNs), which consist of multiple layers (input, hidden, and output layers).

2. Backpropagation: The learning process involves adjusting weights using backpropagation and optimization algorithms like Stochastic Gradient Descent (SGD) and Adam.

3. Activation Functions: Functions like ReLU, Sigmoid, and Softmax help introduce non-linearity, enabling neural networks to learn complex patterns.

4. Data Requirements: DL models require large datasets and significant computational power, often using GPUs or TPUs for training.

5. Popular Architectures:

• Convolutional Neural Networks (CNNs) – Used for image processing.

• Recurrent Neural Networks (RNNs) – Used for sequential data like time series or speech.

• Transformers – Used in NLP (e.g., BERT, GPT).

• Generative Adversarial Networks (GANs) – Used for data generation.

Applications of Deep Learning

• Computer Vision: Facial recognition, object detection, medical imaging.

• Natural Language Processing (NLP): Machine translation, chatbots, sentiment analysis.

• Speech Recognition: Virtual assistants (e.g., Siri, Alexa).

• Autonomous Vehicles: Self-driving car perception and decision-making.

• Healthcare: Drug discovery, disease diagnosis.