How to Build Neural Networks in Python

Trace the history of neural networks from perceptrons to deep learning. Explain how back propagation and hidden layers empower models to recognize patterns and drive applications from vision to speech.

Explore how a neural network processes inputs through input, hidden, and output layers, using perceptron neurons with weights, bias, activation functions, and backpropagation, guided by a cost function to learn.

Set up your Python work environment by using Google Colab, creating a Google account if needed, and launching a new notebook to write and run code.

Harness Google Colab, a cloud-based notebook with pre-installed libraries like TensorFlow and PyTorch, offering GPU and TPU acceleration and easy Google Drive mounting for collaborative ML.

Build a deep neural network with TensorFlow in Python to classify images using Google Colab.

Explore the fashion dataset, a 70,000 labeled image collection at 28 by 28 pixels with ten classes (0–9), including t shirt or top and trouser.

Import TensorFlow, NumPy, and Matplotlib in Google Colab, load the fashion mnist dataset via Keras, and prepare training and test images and labels for image classification.

Define and map ten classes 0-9 to readable names like t shirt and trouser. Examine shapes and counts: 60,000 train and 10,000 test images, each 28 by 28.

Process the data set by inspecting and scaling image pixels to 0–1, then visualize 25 samples in a 5x5 grid to guide neural network training.

Flatten 28 by 28 images to 784 features and build a TensorFlow neural network with two dense layers, 128 and 10 nodes, for ten classes; prepare optimizer, loss, and metric.

Compile the neural network with Adam optimizer and sparse categorical cross-entropy loss, train with fit for ten epochs, then evaluate on test data achieving 88.13% accuracy and make predictions.

Show outputs graphically by plotting each image with its predicted label and a corresponding bar graph. Color-code correct and incorrect predictions using blue and red on a two-panel figure.

Learn to predict and display multiple images and their accuracy by looping over five rows and three columns, plotting images and bar graphs, and validating single-image predictions.

Save and load a trained neural network by creating a directory, using save and load methods, and verifying the load with a summary, evaluation, and accuracy.