Deep Learning with Tensorflow and Angular 2!

Explore Java variable types from integers and longs to floats, doubles, booleans, chars, and strings, and learn declaration and initialization, default values, and the difference between primitive types and strings.

Explore primitive types and strings, assignments, arithmetic and conditional operators, and basic conversions. Learn type casting, string concatenation, and parsing strings to numbers using standard methods.

Explore arrays and lists in Java, highlighting immutability of arrays versus the mutable nature of lists, including array and ArrayList setup, initialization, and basic operations.

Learn static variables and access modifiers in Java, including class-level constants accessed via the class name, and how private access and encapsulation are achieved with methods.

Build and position a basic Android UI using constraint layout, edit text, and text view, then wire up string resources and on click to display results.

Discover how to declare, implement, and call functions in Python, pass parameters, return values, and use promises, while managing starting and current positions in example scenarios.

Begin with TensorFlow basics, learn what it is, install it into Python, and build training and testing models, culminating in a linear regression from scratch.

Explore the key TensorFlow components to build a computational graph and train and test a model. Follow along to build a simple linear regression model in Python.

Save a pre-trained model, import it into Android Studio, and build an app that uses TensorFlow to predict digits from NIST data.

Learn to build a custom estimator function in TensorFlow, defining a model function with features, labels, loss, and a gradient-descent optimizer, then train and evaluate with custom input data.

Compare a custom estimator function to a prebuilt model in a deep learning workflow, covering training, evaluation, prediction, and debugging of mode handling, input defaults, and float types.

Design a simple Android UI that accepts a number and shows the result, wire it to main activity, and configure TensorFlow inference inputs, outputs, input shape, and library loading.

Initialize a TensorFlow inference interface, prepare a float input, run inference on the model, and display the resulting output in the text view after pressing the button.