100+ TensorFlow Coding Exercises for AI Projects

Explore the basics of TensorFlow, install it, and run a basic program while defining constants and variables, performing simple math and matrix operations, with guided coding exercises.

Set up TensorFlow in your environment, verify the installation by importing it and printing the version, then run a session performing arithmetic and matrix operations with constants and variables.

Explore TensorFlow core concepts, including tensors, variables, computation graphs, sessions, placeholders, and model saving, through practical coding exercises and examples.

Define tensors of varying shapes and types, print their properties in TensorFlow sessions; create variables, build a computational graph with operations, use placeholders, and save or restore the model.

Master linear regression with TensorFlow by preparing data, defining a TensorFlow model, computing the loss, applying gradient descent, training, evaluating, and visualizing results with matplotlib.

Generate or load a linear regression dataset and build a TensorFlow model with placeholders, weights and biases; train with gradient descent, evaluate, and visualize results using matplotlib.

Master classification with TensorFlow by preparing data, implementing logistic regression, applying softmax for multi-class problems, computing cross-entropy loss, training, evaluating accuracy, and visualizing decision boundaries.

Generate or load a synthetic three-class dataset, prepare it for TensorFlow processing with train-test split, and build a multi-class logistic regression using softmax with cross-entropy loss and visualize decision boundaries.

Explore neural networks and TensorFlow foundations, learning to define architectures, initialize weights, implement forward propagation, compute loss, and train a classifier on real data to improve accuracy.

Design a neural network architecture in TensorFlow with two hidden layers using ReLU and a no-activation output layer for multi-class classification, including training, cross-entropy loss, Adam optimization, and dropout.

Learn to build and train convolutional neural networks in TensorFlow for image classification, including convolutional and pooling layers, MNIST dataset, visualization, and fine-tuning.

Build a cnn in TensorFlow for mnist using convolutional layers with relu and max pooling, then train with cross-entropy loss and Adam optimizer and visualize feature maps to fine-tune accuracy.

Explore recurrent neural networks for processing sequences and time series, design neural network architectures in TensorFlow, implement lstm cells, and train on data via coding exercises to predict future values.

Build an rnn model in TensorFlow using tf.keras.layers with units and sequence length, train on normalized time series data, and generate predictions with a dense output layer.

Explore transfer learning with TensorFlow by loading pre-trained models, freezing layers, adding custom layers, and fine-tuning for your dataset while evaluating performance against training from scratch.

Explore transfer learning with pre-trained models like Vgg16 on ImageNet, freezing layers, adding custom dense and dropout layers, preprocessing data, training, evaluating, and comparing to a from-scratch baseline.