Advanced Neural Networks
Explore cutting-edge architectures and optimization techniques for state-of-the-art performance.
Advanced Architectures
Modern neural networks use sophisticated architectures to achieve breakthrough performance on complex tasks.
Key Architectures:
- Attention mechanisms: Focus on relevant information
- Residual connections: Enable very deep networks
- Capsule networks: Better spatial relationships
- Neural Architecture Search: Automated design
- Graph Neural Networks: Process graph-structured data
Advanced Training Techniques
Sophisticated training methods unlock better performance and efficiency in neural networks.
Optimization Strategies:
- Mixed precision training: Faster computation
- Gradient accumulation: Handle larger batches
- Learning rate warm-up: Stable early training
- Layer-wise learning rates: Fine-tuned control
- Self-supervised learning: Learn without labels
Attention Mechanism Example
import tensorflow as tf
from tensorflow import keras
class AttentionLayer(keras.layers.Layer):
def __init__(self, units):
super().__init__()
self.W = keras.layers.Dense(units)
self.U = keras.layers.Dense(units)
self.V = keras.layers.Dense(1)
def call(self, query, values):
# query: (batch, query_dim)
# values: (batch, time, value_dim)
# Expand query to match values time dimension
query_expanded = tf.expand_dims(query, 1)
# Calculate attention scores
score = self.V(tf.nn.tanh(
self.W(query_expanded) + self.U(values)
))
# Apply softmax to get attention weights
attention_weights = tf.nn.softmax(score, axis=1)
# Weighted sum
context = attention_weights * values
context = tf.reduce_sum(context, axis=1)
return context, attention_weightsSign up for free to track which lessons you've completed and get learning reminders.