Deep Learning

Create a model for binary classification (XOR problem).

Train a model with batch size of 2.

Create an optimizer with learning rate 0.001.

Create a constant tensor with value [1, 2, 3].

Create a trainable variable initialized to 5.0.

Evaluate a trained model on test data.

Import TensorFlow and print its version.

Create a model to solve y=2x problem.

Load data from a CSV file using pandas.

Use a trained model to predict output for input 3.

Multiply two 2x2 matrices.

Create a simple model and print its summary.

Normalize data to range [0, 1].

Convert labels [0, 1, 2] to one-hot encoding.

Add two tensors [1,2] and [3,4].

Save a model to file and load it back.

Create a model with just one dense layer (neuron).

Split XOR data into features (inputs) and labels (output).

Train a model on dummy data for one epoch.

Compile a model with mean absolute error loss.