Deep learning is a subset of machine learning. And Machine learning also a subset of artificial intelligence (AI). It use neural networks with many layers to model complex patterns in data.. Deep learning is inspired by how the human brain works.
Here’s a simple step-by-step breakdown to understand it:
Understanding Neural Networks
Neurons: Just like in the brain, a neural network is made up of neurons (also called nodes). Each neuron receives inputs, processes them, and passes on an output.
Layers: Neurons are organized into layers. The simplest neural network has three types of layers:
- Input Layer: Receives the data (e.g., images, text).
- Hidden Layers: These layers do the heavy lifting of processing and finding patterns.
- Output Layer: Produces the final prediction or result.
2. Learning through Training
- Weights and Biases: Each connection between neurons has a weight that determines how strongly the input should influence the output. Neurons also have a bias, which shifts the output.
- Forward Propagation: Data moves through the network from the input layer to the output layer. Each neuron processes the inputs using a mathematical function (activation function) and passes the result to the next layer.
- Loss Function: The network’s output is compared to the actual result using a loss function. This measures how far off the network’s prediction is from the target.
3. Improving the Network
- Backpropagation: To improve accuracy, the network adjusts the weights and biases using a technique called backpropagation. It calculates how much each weight contributed to the error and updates them to reduce the error.
- Gradient Descent: This is the method used to find the optimal weights and biases. The network takes small steps (learning rate) in the direction that reduces the error the most.
4. Deep Networks
- Multiple Hidden Layers: What makes deep learning “deep” is the use of many hidden layers. Each layer extracts more complex features from the data.
- Feature Hierarchies: In a deep network, early layers might detect simple features (like edges in an image), while deeper layers recognize more complex patterns (like shapes or objects).
5. Training on Big Data
- Data Requirements: Deep learning models often require a lot of data to perform well because they have many parameters to learn.
- GPU Acceleration: Training deep networks is computationally expensive, so GPUs (Graphics Processing Units) are often used to speed up the process.
6. Applications of Deep Learning
- Image Recognition: Identifying objects, faces, or scenes in images.
- Natural Language Processing (NLP): Understanding and generating human language.
- Speech Recognition: Converting spoken language into text.
- Autonomous Vehicles: Processing sensor data to navigate and make decisions.
Summary
Deep learning involves building complex neural networks that can automatically learn patterns from large amounts of data. The network improves by adjusting its internal parameters, and with enough data and training, it can perform tasks like image and speech recognition, often surpassing traditional machine learning methods.