Learn to build caltech-101 image classifier mobile app

Explore how deep learning lets machines identify objects in images by learning hierarchical features through neural networks, from basic parts to faces, with auto driving and medical industry applications.

This lecture distinguishes AI, machine learning, and deep learning as distinct, explains machine learning trained on data, and deep learning uses neural networks to learn features for image classification.

Explore how perceptrons fail nonlinear separation, illustrated by xor gate, and trace the rise of backpropagation and convolutional neural networks from zip code to Alex Net.

Explore the mathematics of logistic regression, including the cost function, sigmoid output, and gradient descent to minimize prediction error in binary classification.

Master gradient descent to minimize loss by updating weights and biases with a learning rate, using forward and backward propagation in neural networks and computation graphs.

Explore how linear regression concepts model real estate prices with a neural network, using inputs like house size and ReLU activation, and discuss convolutional, recurrent, and long short-term networks.

Logistic regression acts as a binary classifier for images, learning weights from raw pixel inputs to distinguish cats from dogs, using a dot product, activation, and gradient-based weight updates.

Learn to build multilayer perceptron neural networks with input, bias, and hidden layers, using forward propagation and backpropagation to train with activation functions like sigmoid and relu to minimize loss.

this lecture explains convolutional neural networks (cnn) for image classification, outlining convolutional layers with filters, max pooling, relu activations, fully connected layers, and softmax for multiclass outputs.

Download a labeled Caltech-101 data set, train a custom model with training, validation, and testing, then export for a mobile image classifier.

Explore accuracy, recall, precision, and F1 score as metrics for binary classification, including how accuracy alone can be misleading and how true positives, false positives, and false negatives affect performance.

Create a new Xcode project in Swift, add a navigation controller, image view, and label, and wire a camera button with outlets to display objects.

Import the machine learning model into the mobile app by dragging it into Xcode, which creates a Swift class and exposes a three-channel RGB image input and a label-confidence output.

Enable camera and photo library permissions, check camera availability on real devices or simulators, and present alerts to guide users when permissions or hardware are unavailable.

Capture images from the camera or photo library and present selection options. Prepare the chosen image by adjusting orientation for a machine learning model to detect objects.

Apply Core ML and Vision to run ML inference on camera images using the Caltech model, retrieve classifications with labels and confidence, and update the UI on the main thread.

Run the Caltech-101 image classifier mobile app on a real device, performing on-device inference from camera and photo library to classify images without Wi-Fi or cellular connectivity.

Build a caltech-101 image classifier mobile app by training on labeled image data using convolutional neural networks and a multilayer perceptron, with on-device offline inference.