Layers

In the context of neural networks, a layer is a structured arrangement of neurons (nodes), typically organized in rows. Each neuron in a layer is connected to neurons in the previous and next layers. These connections are characterized by weights, which are adjusted during training.

Model Compilation

• Optimizer: Determines how the network will be updated based on the loss function. It implements the specific variant of the gradient descent algorithm (backpropagation step).
• Loss Function: Measures how well the model is performing. A model aims to minimize this function.
• Metrics: Used to monitor the training and testing steps. Unlike the loss function, metrics are not used for training the model but for evaluating its performance.

Model Training

Overfitting

Overfitting is a scenario where a model learns the training data too well, including its noise and random fluctuations. This typically results in excellent performance on the training set but poor performance on unseen data.

Underfitting

Underfitting happens when a model is too simplistic, failing to capture the underlying patterns and complexities in the data. This leads to subpar performance on both the training set and new data.

Bias-Variance Tradeoff

Regularization

Norm Penalties

• Overview: Norm penalties impose a constraint on the weights of the network. They are added to the loss function and penalize large weights, encouraging the model to find simpler functions that may generalize better.
• Key Methods: L1 regularization (Lasso), L2 regularization (Ridge), L1L2 regularization (Elastic Net).

Dropout

• Overview: Dropout randomly deactivates a subset of neurons during training, which prevents the network from relying too heavily on any specific neuron and encourages it to learn more robust features.
• Key Methods: Dropout, Spatial Dropout, Variational Dropout.

Batch Normalization

• Overview: It normalizes the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation.

Optimizers

Fine-Tuning

Feature Extraction

Multitask Learning

Congratulations!

You've successfully completed the Neural Network with TensorFlow course, marking a significant milestone in your journey into deep learning. Your dedication and hard work have equipped you with the skills to build and optimize sophisticated neural network models.

Keep exploring, learning, and applying your knowledge to solve real-world problems. Well done on achieving this remarkable feat!