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 as of 2024. The Importance of Weight Initialization Weight initialization is a crucial step in training neural networks. It involves setting the initial values of the weights before the learning process begins. If weights are not initialized properly, the training process can suffer from issues like slow convergence, vanishing or exploding gradients, and suboptimal performance. To address these challenges, researchers have developed various initialization methods, each tailored to specific activation functions and network architectures. Classic Initialization Strategies LeCun Initialization LeCun Initialization, introduced by Yann LeCun, is particularly effective for networks using the SELU activation function. It initializes weights using a...

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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(), activation='tanh')) # Using Glorot Normal model.add(Dense(64, kernel_initializer=GlorotNormal(), activation='tanh')) 2. He Initialization He Initialization is optimized for ReLU and its variants, ensuring that the gradients remain within a good range across layers. Mathematical Formula: Uniform Distribution: Normal Distribution: Code Example in Keras: from tensorflow.keras.initializers import HeUniform, HeNormal # Using He Uniform model.add(Dense(64, kernel_initializer=HeUniform(), activation='relu')) # Using He Normal model.add(Dense(64, kernel_initializer=HeNormal(), activation='relu')) 3. LeCun Initialization LeCun Initialization is used for the SELU activation function, maintaining the self-normalizing property of the network. Mathematical Formula: Normal Distribution: Code Example in Keras: from tensorflow.keras.initializers import LecunNormal # Using LeCun Normal model.add(Dense(64, kernel_initializer=LecunNormal(), activation='selu')) Summary Table:...

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