1st-Neural-Network-

Dense

A layer where each neuron is connected to every neuron in the previous layer

Example

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(10, input_shape=(5,), activation='relu')  # 10 neurons, input shape of 5 features
])

Loss

Measures how good a neural network model is in performing a certain task
It calculates the difference between the predicted output and the actual output. The goal of training a neural network is to minimize this loss

Example

Common Loss Functions:

• Mean Squared Error (MSE): Used for regression tasks.
• Categorical Crossentropy: Used for classification tasks

from tensorflow.keras.losses import MeanSquaredError

loss_fn = MeanSquaredError()

Optimizer

Find minmum values of functins A function or an algorithm that adjusts the attributes of the neural network, such as weights and learning rates
An optimizer adjusts the weights of the neurons in the neural network to minimize the loss. 

Common Optimizers:

• SGD (Stochastic Gradient Descent):
• Adam:
• RMSprop:

from tensorflow.keras.optimizers import Adam
optimizer = Adam(learning_rate=0.001)