Deep Learning Project Building with Python and Keras

Explore the Android Studio interface, navigate the project and file structure, and learn key concepts like project windows, activity and layout files, comments, log statements, and running an emulator.

Learn to set up an Android emulator, create a virtual device (Pixel, API 25), and build and run your app, understanding emulator limits and testing on real devices.

Explore arrays and lists in Java, contrast immutable arrays with mutable lists such as array lists, and learn setup, syntax, and basic operations like add, remove, and element modification.

Master control flow in programming through if and switch statements, including else, else-if, and operators, with practical traffic-light examples.

Learn how to use while loops to repeat code until the condition becomes false, tally numbers, and iterate through arrays, with break and continue examples.

Declare, implement, and call functions in Java, using parameters, return types, and function types. Explore a bank purchase example with a global bank balance and item costs, illustrating control flow.

Explore how to define functions with return types and parameters, use promises, overload with different inputs, return booleans, and understand scope and parameter placeholders.

Learn the basics of classes and objects in Java, including fields, methods, and constructors that define an object's state and behavior, acting as a blueprint for new instances.

Explore how a subclass inherits all attributes and behavior from a superclass, adds new features like strobe settings, and uses constructors and method overriding in a light class example.

Build a basic Android UI by adding an edit text and a button in constraint layout, wiring IDs, and displaying input in a text view using string resources.

Connect the user interface to the backend by accessing the edit text and text view in the main activity, then trigger display results with an on click method.

Handle input by capturing added text and displaying it in a text view with input validation, then beautify the UI with color, padding, and text size for an Android app.

Explore PyCharm by creating and opening Python files, using the project interpreter, running code, and installing packages through plugins to manage Python versions and dependencies.

Master Python variable operations and conversions, including assignment, not, arithmetic, modulus, and compound assignments, plus string concatenation and type conversions.

Explore operations on tuples, lists, and dictionaries, including retrieving, adding, deleting, and modifying elements, and using length, min, max, and dictionary views like keys, values, and items.

Explore while and for loops, including how to implement loops, avoid infinite loops, use break and continue, and iterate through arrays.

Explore variable nodes and how they differ from constant nodes, learn to manually reassign values, and initialize variables with the TensorFlow global variables initializer for model training.

Learn how placeholder nodes serve as inputs in computational graphs, requiring runtime values via a feed dictionary, with enforced shape and data type to ensure valid inputs.

Explore operation nodes in a TensorFlow computation graph, using placeholders, constants, and variables, and build a mini graph with add, multiply, and other operations.

Explore loss functions and optimizers, learn how training minimizes the difference between actual and expected outputs, and see gradient descent in action for a linear regression model.

Develop a simple linear regression model in TensorFlow by building w and b, defining placeholders, a squared loss, gradient-descent training, and testing with synthetic data.

Explore the MNIST dataset to build a digit recognition app, first in Python with a simple linear regression model, then deploy a saved model in Android Studio.

construct a computational graph for a simple neural network: define x inputs, initialize w and b, compute y via matrix multiply, and train with softmax cross-entropy loss using gradient descent.

Train the model using a training loop with batches, evaluate accuracy on the test data, and generate predictions, then prepare for saving and freezing the graph for Android export.

Set up an Android Studio project, import the optimized model and resources, adjust API 25 build settings, and integrate the TensorFlow inference library for model deployment.

Build an Android UI with constraint layout, featuring an image view, a text view, and buttons. Connect to main activity and prepare string resources and onClick handlers for backend integration.

Load and display digits 0–9 28 by 28 images from resources in a ui with predict and load next buttons, and format them for model input.

Learn to perform predictions by feeding a pixel buffer into a machine learning model, running inference, and extracting a 10-element output array to display readable results.

Display the model's top two predictions by computing the maximum and second-maximum labels from the inference results, formatting them in a text view for clear user feedback.