- [Instructor] In the introductory video
about deep learning, we established that it relies
on artificial neural networks, ANNs,
which are composed of layers
of interconnected nodes or artificial neurons.
This component of the network plays a distinct role
in our deep learning systems learn and make predictions.
Neural networks perform mathematical calculations
similar to how the brain processes information.
True interconnected neurons.
While the specific arrangement of layers
and connection may vary depending on the model architecture,
most neural networks share these essential
components input layer.
This is the entry point for raw data such as medical images,
genomic sequences, or electronic health records.
It pre=processes the data and passes it
to the subsequent layer for analysis.
For example, in analyzing X-ray images,
the input layer would represent pixel values
of the image.
When coding from scratch,
you must define values like the size
and dimension of the input data.
If using the pretrained model, the data should be adjusted
to match the models required input format.
Hidden layers.
These layers contain neurons
that perform computations using weighted input
and activation functions.
They extract hierarchical features from the input data.
Early layers might detect simple patterns,
e.g. edges in the medical image.
Deeper layers identify more abstract representations.
For example, identifying tumors.
Output layer.
The final layer produces the models prediction
or result such classifying an X-ray image
as normal or abnormal.
Its purpose is to map the length features
to specific outcomes.
For instance, the network would assign probabilities
to each possible diagnosis.
Weight and biases.
These are the parameters that determine the strength
of connections between neurons
and influence the output of each layer.
Weight represent the importance of specific compute,
while biases help fine-tune predictions.
These parameters are adjusted during training
to minimize errors and improve accuracy.
Activation functions.
These are the mathematical functions
that decide whether a neuron fires
and passes its information to the next layer.
Activation function introduced non-linearity,
enabling the network to model complex relationships,
such as detecting a rare disease in a dataset
with diverse patient characteristics.
Common examples include, ReLU, rectified linear units.
ReLU passes positive values while discarding negatives,
helping the network to focus on useful patterns.
Soft marks, soft mark converts outputs into probabilities.
Useful for classification task.
Sigmoid.
Sigmoid maps values between zero and one,
and it's often used for binary classification.
Hyper parameters.
These are adjustable settings
that influence the model structure and behavior.
Think of them as tuning knobs for optimization.
Some of them include the number of neurons in hidden layers,
which determines the model's capacity to process information
affecting both the complexity and computational demand.
The choice of activation function impacts
our patterns are identified.
The learning rate controls how quickly weights
and biases are updated during training.
Understanding these hyperparameters is critical
for designing neural networks optimized
for healthcare applications,
such as balancing diagnostic accuracy
with computational efficiency.