Deep Leaning An Overview of Concepts & Application

Deep Leaning An Overview of Concepts & Application

1. Deep Learning An Overview of Concepts, Architectures, and Applications

2. Introduction • Deep Learning is a subset of Machine Learning based on Artificial Neural Networks. • It automatically learns features from raw data. • Inspired by the human brain and its neural connections. • Powers many modern AI applications.

3. Key Characteristics of Deep Learning • Uses multiple layers of neurons (deep networks). • Learns hierarchical feature representations. • Requires large amounts of data and computational power. • Enables end-to-end learning.

4. Deep Learning Architectures • Feedforward Neural Networks (FNN) • Convolutional Neural Networks (CNN) • Recurrent Neural Networks (RNN) • Long Short-Term Memory (LSTM) • Transformers

5. Training Deep Networks • Forward Propagation • Loss Function • Backpropagation • Gradient Descent & Optimizers (SGD, Adam) • Regularization (Dropout, Batch Normalization)

6. Applications • Computer Vision (image classification, object detection) • Natural Language Processing (machine translation, chatbots) • Speech Recognition & Generation • Healthcare (disease detection, drug discovery) • Autonomous Vehicles • Recommendation Systems

7. Advantages & Challenges Advantages: • High accuracy with large data • Learns complex features automatically • Broad applicability across domains Challenges: • Requires large datasets and GPUs/TPUs • Black-box nature (lack of interpretability) • Risk of overfitting • High energy consumption

8. Future of Deep Learning • Explainable AI (XAI) • Integration with Quantum Computing • Efficient models for edge devices • Multimodal learning (vision + text + speech) • Continual and self-supervised learning