Learn how to build pose detection deep learning iPhone app

Gain a math-light, intuitive introduction to deep learning, where machines learn patterns via deep neural networks and automatically extract features for tasks like image recognition and medical imaging.

Explore how artificial intelligence differs from machine learning and deep learning, and learn how deep learning automatically learns features through neural networks with examples like image classification.

Deep learning is taking off due to faster hardware like GPUs enabling parallel vector computations and a surge in available data, which together boost training speed and accuracy.

Explore using deep learning to model real estate prices with linear regression and simple neural networks, using features like house size and location, and relu activation.

Learn how logistic regression acts as a binary classifier inside neural networks by feeding image pixels, adjusting weights via the gradient of loss to minimize error toward the true label.

Learn how logistic regression uses a sigmoid to squash outputs to zero–one for binary classification, and how the cost function is minimized with gradient descent.

Master gradient descent to minimize loss in neural networks, updating weights and biases using derivatives and a learning rate, while performing forward and backward propagation along a computation graph.

Learn how to apply a multi-layer perceptron to tasks like sentiment analysis and real estate price prediction, using inputs, bias, weights, activation, and forward and backpropagation.

Learn how convolutional neural networks extract features from images through convolutional layers, pooling, and ReLU activations, culminating in a fully connected layer with softmax for multiclass classification.

Explore posenet, a deep learning model that identifies key joints and connections in human poses. Compare top-down and bottom-up approaches, from bounding boxes to joint heatmaps and offsets.

Develop a pose detection deep learning iPhone app that captures camera frames in real time, runs predictions, and renders live joint coordinates with connected lines.

Set up the iPhone video capture pipeline for a pose detection app by creating a Swift project, configuring AVFoundation, and implementing frame delivery via a video capture delegate.

Continue setting up video capture by implementing a public recapture function and internal helpers, configuring input and output with the built-in camera, and handling errors via delegates.

Set up video capture for a pose detection iPhone app by mapping device orientations, managing capture sessions, and delivering CGImage frames via a delegate for real-time deep learning.

Download the PoseNet model from Apple's website, import it into the Xcode project, and configure the input to output the 17 key joints coordinates per frame for real-time pose rendering.

Explore posenet output for a pose estimation iPhone app. Learn how 17 joints use offset, heat map, and forward and backward displacement vectors to enable multi-person pose estimation.

Define the PoseNet output structure with heat maps, offsets, and joint coordinates for a swift iPhone app, then implement extensions to extract features from predictions.

Implement modular pose net output by adding extension functions to compute joint positions from model offsets, introducing a joint class and handling 17 joints, x and y offsets, and indices.

Develops PoseNet output handling, implementing forward and backward displacement maps and heat map offsets to compute joint positions for a pose-detection iPhone app.

Develop a poseNet model inference module that runs in the background, processes each image frame for pose predictions, and notifies a delegate when results are ready for visualization.

Learn to configure pose detection parameters and build a pose structure for single or multi-person images, defining joints and edges with confidence thresholds and refining poses using Core Graphics.

Learn to build a pose detection iPhone app by implementing a pose builder that handles a transformation matrix, scales input images to the model's required size, and outputs transformed results.

Extend the single pose builder in Swift to compute the best joint positions by averaging confidence scores, then apply image transformation and scaling to align joints with the input image.

Extends a pose builder to handle multiple poses per frame by evaluating joint candidates over a grid with forward and backward displacement maps and graph traversal to maximize confidence.

Traverse the joint graph to assemble poses and update joint properties, then compute the greatest confidence within a local radius and filter non-overlapping joints for multi-pose building.

Implement a multiple pose builder for a pose-detection iPhone app by selecting joint candidates with the highest confidence, updating joint positions, and mapping detections back to the original image.

Learn to build and connect a configuration view for a pose detection iPhone app, including navigation, outlets, and controls for single vs multiple pose modes, joint thresholds, and stack-based UI.

Initialize UI labels and sliders from the configuration object, reflecting threshold, confidence, minimum distance, local search radius, and offset, and wire actions to apply changes.

Explore how to implement a popover presentation using a Swift class derived from a presentation controller, controlling the presenting and presented view controllers, and sizing a 60 percent pop-up.

Learn to implement a pose detection iPhone app by building a live camera view with a flip camera button and rendering pose joints and segments on a custom image view.

Learn to build a pose detection iPhone app by implementing image context flipping, drawing joints and lines, and wiring camera controls, navigation, and video capture.

Extend the main view controller to handle segues to the configuration view, implement video capture and prediction delegates, and display pose detections with confidence thresholds while requesting camera access.

Run the pose detection deep learning iPhone app, grant camera permissions, and observe real-time joint tracking for single and multiple poses, with adjustable confidence thresholds.

Apply pose detection with a deep learning iPhone app by reviewing frame-by-frame predictions from PostNet, adjusting joint thresholds and radii, and exploring CNN fundamentals and practical demos.