iOS Machine Learning with TensorFlow Lite – Build AI Apps

Explore the fundamentals of machine learning, a branch of ai that uses data and algorithms to identify patterns and make predictions, from dog breed recognition to salary forecasting.

Explore unsupervised learning with unlabeled data and clustering to reveal purchase patterns and optimize product placement, then study reinforcement learning where agents learn by trial and error via rewards.

Explore how a neural network learns to predict house prices from area and rooms by feeding data, adjusting weights through backpropagation, and evaluating epochs for model training.

Explore machine learning concepts, including supervised and unsupervised learning, deep learning with neural networks, feed forwarding and back propagation, learning rate, loss function, and strategies like dropout to prevent overfitting.

Learn to create and delete Python numeric variables, assign values, and check types, then explore integer, long, float, and complex numbers with real and imaginary parts.

Explore Python lists, the most versatile data type in Python, including creating multi-type and nested lists, indexing, slicing, and using core methods like append, insert, remove, count, index, sort, and reverse.

Compare Python tuples and lists, noting that tuples are immutable and enclosed in parentheses, while lists use square brackets. Create dictionaries with unique keys and access values by key.

Learn Python file handling for saving tflite models for iOS apps, including opening files with modes r, w, wb, rb, and a, reading, writing, and closing files.

Learn numpy, a python library for creating and manipulating arrays for data preparation. Import numpy as np to build 1D and 2D arrays, inspect shape and size, and set dtype.

Explore the pandas library for Python, learn to create and manipulate data frames, and convert lists and dictionaries into tabular data for data analysis.

Load a csv into a pandas data frame using read_csv, upload nba.csv, and inspect 458-row, 9-column NBA players dataset with name, team, number, position, age, height, weight, college, and salary.

Compute means in TensorFlow Lite with tf.reduce_mean, using v1 or v2 as inputs. Use axis 0 for column means and axis 1 for row means on a 2D tensor.

Generate random tensors in TensorFlow with random normal and uniform, specifying shape, mean, stddev, and data type; seed for reproducibility; then add, concatenate, and identify min/max indices with argmin/argmax.

Learn how TensorFlow checkpoints save and restore thousands of variable values during model training, using tf.train.checkpoint to save and checkpoint.restore to recover progress.

Learn to train a simple linear regression model on data with the pattern 2x minus 1, and deploy it to an iOS app using TensorFlow Lite for on-device predictions.

Train your machine learning model on a simple regression task where y equals two into x minus one, using TensorFlow, Keras, and NumPy, convert it to TensorFlow Lite for mobile.

Import libraries, prepare a dataset with one input and one output, train a neural network with one layer and a single neuron, test it, and convert to tflite for iOS.

Build an iOS SwiftUI app that uses a TensorFlow Lite regression model to compute output from a single input (output = 2*input−1), displayed in a text view after tapping predict.

Add the linear.tflite model to the iOS project in a models group, install CocoaPods, install TensorFlow Lite Swift, and import TensorFlow Lite to load the model.

Load a TensorFlow Lite model in an iOS app by creating and initializing a TensorFlow Lite interpreter from the main bundle, then allocate tensors and confirm the runtime initializes.

Integrate a TensorFlow Lite model into an iOS app by loading with an interpreter, allocating tensors, processing input data, invoking the model, and displaying the output.

Train a regression model to predict fuel efficiency for iOS apps using Kaggle's auto mpg dataset, with features such as cylinders, displacement, horsepower, weight, acceleration, model year.

Apply one hot encoding to convert the origin categorical column into binary USA, Europe, and Japan columns, then remove the origin column and observe improved model accuracy.

Split the dataset into 80% training and 20% testing using sample and drop, then separate miles per gallon as the label and inspect features with describe for normalization.

Normalize numeric data in a data set via normalization to a common range before model training, preventing large features like area from dominating the model.

Normalize each column by subtracting its mean and dividing by its standard deviation using the train stat per column, applied via the norm function to training and test data.

Build and train a tf-keras sequential neural network with two 64-neuron hidden layers on normalized data, using activation, mse and mae metrics, then summarize and fit to monitor loss decline.

Build a Swift iOS GUI for a fuel efficiency prediction app, with input fields for cylinders, displacement, horsepower, weight, acceleration, year, region, and an mpg result after predict.

Learn to load a fuel efficiency tflite model in a Swift iOS app by adding automobile.tflite to the main bundle, importing TensorFlow Lite, and initializing the interpreter.

Prepare nine input features for a TensorFlow Lite model in a Swift iOS app by converting user entries to floats and using one-hot encoding for USA, Europe, and Japan.

Pass inputs to the tflite model in the exact training order—cylinders, displacement, horsepower, weight, acceleration, model year, and origin USA/Europe/Japan—and convert to data format before reading the fuel efficiency.

Ensure the input order matches the training data, apply the same normalization, and handle categorical columns with one-hot encoding by creating separate variables for each category (USA, Europe, Japan).

Collect and preprocess a house price dataset with longitude, latitude, house median age, rooms, income, and ocean proximity. Train a multi-layer neural network and deploy a tflite model in iOS.

train a house price prediction model using a provided dataset in Colab, loading data with pandas and TensorFlow, handling missing values, one-hot encoding, and an 80/20 train-test split.

Normalize the dataset, build a three-hidden-layer neural network, train and evaluate it for house price prediction, convert to tflite, and compare models in a Swift iOS app.

Retrain the price prediction model by adding more neural network layers and increasing neurons, convert to tflite, and compare training and testing loss to identify overfitting.

Create a house price prediction iOS app by starting a new Xcode SwiftUI project, replacing ContentView.swift, and wiring inputs for longitude, latitude, age, rooms, bedrooms, population, income, and ocean proximity.

Explore the gui of a house price prediction iOS app built with SwiftUI, featuring a scroll view, vstack, longitude and latitude inputs, a five-option dropdown, and a predict button.

Test a house price prediction application in a simulator using a test dataset with longitude, latitude, age, rooms, bedrooms, population, income, and ocean proximity inland; verify predictions against actual prices.