Deep Learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.
Key Concepts
- Neural Networks: Deep learning uses neural networks with many layers (hence the term "deep") to process and learn from data.
- Training Data: Large amounts of data are used to train the neural networks, allowing them to learn patterns and make predictions.
- Overfitting: A common problem in deep learning is overfitting, where the model learns the training data too well, including the noise, and performs poorly on new data.
Books to Read
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This is a comprehensive book covering the fundamentals of deep learning, neural networks, and their applications.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron: This book is a practical guide to machine learning with a focus on deep learning using popular Python libraries.
Resources
- Deep Learning Specialization by Andrew Ng on Coursera: This specialization offers courses in deep learning from Andrew Ng, a leading expert in the field.
Deep Learning Neural Network