Weight initialazation part 2 – day 23

Understanding Weight Initialization Strategies in Deep Learning: 2024 Updates and Key Techniques Understanding Weight Initialization Strategies in Deep Learning: 2024 Updates and Key Techniques Deep learning has revolutionized machine learning, enabling us to solve complex tasks that were previously unattainable. A critical factor in the success of these models is the initialization of their weights. Proper weight initialization can significantly impact the speed and stability of the training process, helping to avoid issues like vanishing or exploding gradients. In this blog post, we’ll explore some of the most widely-used weight initialization strategies—LeCun, Glorot, and He initialization—and delve into new advancements...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
32

Weight initialisation in Deep Learning well explained _ Day 21

  Weight Initialization in Deep Learning: Classic and Emerging Techniques Understanding the correct initialization of weights in deep learning models is crucial for effective training and convergence. This post explores both classic and advanced weight initialization strategies, providing mathematical insights and practical code examples. Part 1: Classic Weight Initialization Techniques 1. Glorot (Xavier) Initialization Glorot Initialization is designed to maintain the variance of activations across layers, particularly effective for activation functions like tanh and sigmoid. Mathematical Formula: Uniform Distribution: Normal Distribution: Code Example in Keras: from tensorflow.keras.layers import Dense from tensorflow.keras.initializers import GlorotUniform, GlorotNormal # Using Glorot Uniform model.add(Dense(64, kernel_initializer=GlorotUniform(),...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
414