The Medium.com AI blog team unpacks deep learning principles via neural networks, detailing weights, biases, and activation functions. It surveys sampling methods for images, audio, text, and IoT data, and links math foundations to applications in computer vision, speech emotion detection, and NLP.
Key points
- Explains neural network architecture: input, hidden, and output layers with weighted connections and activation functions.
- Details data sampling methods: pixelization for images, frame sampling for video, audio snapshots, and IoT time-series collection.
- Highlights mathematical foundations: linear algebra for matrix operations, probability for predictions, and calculus for gradient-based backpropagation optimization.
Q&A
- What distinguishes deep learning from traditional machine learning?
- How do activation functions influence neural network performance?
- Why is sampling important across different data types?
- What role does backpropagation play in training deep networks?
- How do CNNs differ from RNNs in handling unstructured data?
