Summary
Deep learning is a subset of machine learning that uses neural networks to process large-scale data for tasks like image and speech recognition, natural language processing, and recommendation systems.
A neural network consists of layers of nodes where each node performs weighted sums of its inputs, applies activation functions like ReLU or sigmoid, and produces an output.
Backpropagation is the primary algorithm for training neural networks by minimizing error through Gradient Descent. Regularization techniques, such as dropout, prevent overfitting.
Popular frameworks like PyTorch and TensorFlow facilitate deep learning model development.
Questions:
- What is the role of gradient-based optimization in training deep learning models.
- Explain different gradient descent algorithms, their advantages, and limitations.
Areas of Deep Learning:
Follow
- How does the choice of activation function affect the performance of deep learning models across different tasks?
- What are the trade-offs between different gradient descent algorithms (e.g., SGD vs. Adam) in training neural networks? See Optimisation techniques.
Related
- Transfer Learning: Applying pre-trained models to new tasks.