Image recognition
Image recognition is the computational task of identifying or classifying objects, patterns, scenes, or other meaningful content in images.
Grokking Machine Learning
The computational task of identifying or classifying objects, patterns, or other content in images.
- How it is used
- Uses image recognition to illustrate classification, from detecting a dog to classifying handwritten digits.
- How deep it goes
- Defines pixel-based classification and demonstrates MNIST preprocessing, training, and prediction, while noting perceptron limitations.
- Examples
- Uses dog detection and handwritten-digit classification, including a 3 misclassified as an 8.
- What you can do after
- Apply via Keras by preprocessing MNIST images and labels, training a neural network, and predicting digit classes.
Read how Grokking Machine Learning explains image recognition →
The AI Pocket Book
The task of assigning an image to one of several possible classes based on its visual content.
- How it is used
- Frames image recognition as supervised image categorization, then uses CNNs to explain how visual features become class predictions.
- How deep it goes
- Explains pixels, learned filters, convolution, downsampling, and abstraction, but stops short of mathematical derivations or implementation details.
- Examples
- Uses strawberry, plane, dog, and cat images, plus learned filters for blur, color, edges, and directional lines.
- What you can do after
- Evaluate/understand how CNNs classify images and recognize labeling, generalization, overfitting, and underfitting risks.
Related concepts
- MNIST datasetuses · MNIST is a labeled dataset of 28-by-28-pixel grayscale images representing handwritten digits from zero through nine.
- Geminiimplemented by · Gemini is Google's family of multimodal generative AI models and the conversational AI services built around them.
- GPT-4oimplemented by · GPT-4o is an OpenAI multimodal generative model in the GPT family that processes and generates text and other supported modalities.
- perceptronused by · A perceptron is a single-layer computational model and linear classifier that combines weighted inputs with a bias and applies a threshold or activation rule.
- clusteringused by · Clustering is the technique of grouping data items according to similarity or shared characteristics.
- deep learninguses · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.