[Projeto] modelo.compile(optimizer=tf.keras.optimizers.Adam (1e4), loss='sparse_categorical_crossentropy' , By GEMINI

        modelo.compile(optimizer=tf.keras.optimizers.Adam (1e4), loss='sparse_categorical_crossentropy'  ,
                                                                                                                                                                By GEMINI
                                
        This line is used to configure your TensorFlow/Keras model for training. Here's a breakdown of what each part does:

        1.   modelo.compile(...): This function is used to specify the training configuration of your 
        model. It takes several arguments that define how the model should be trained.

        2.   optimizer=tf.keras.optimizers.Adam (1e-4): This specifies the optimization algorithm 
        to use during training.

            o  tf.keras.optimisers.Adam: This refers to the Adam optimizer, a popular algorithm 
             for training deep learning models. It's known for its efficiency and effectiveness.

            o  (1e-4): This sets the learning rate for the Adam optimizer. The learning rate controls 
                how much the model's weights are adjusted during each training step. A lower learning 
                rate (like 1e-4, which is 0.0001) means the model learns more slowly but can potentially 
                find better solutions.
                
            3.   loss='sparse_categorical_crossentropy' : This defines the loss function that the 
            model will try to minimize during training.

                    o  sparse_categorical_crossentropy: This loss function is typically used for multi-
                        class classification problems where the target labels are integers representing the
                        different classes (e.g., 0 for class A, 1 for class B, etc.). It's appropriate when your labels 
                        are not one-hot encoded.
                            
                            
                4.  metrics=['accuracy']:  This specifies the metrics that will be used to evaluate the 
                performance of your model during and after training.


                                o    ['accuracy']: This indicates that you want to track the accuracy of the model, which 
                                is the percentage of correctly classified examples.

In summary:

This code is configuring your model to be trained using the Adam optimizer with a learning rate of 0.0001, aiming to minimize the sparse categorical cross-entropy loss, and tracking the accuracy metric during training. This setup is common for multi-class classification tasks.

Sources 1.keras.io/api/metrics/ 2.www.tensorflow.org/guide/keras/training_with_built_in_methods