Batch normalisation – trainable and non trainable – day 27

Demystifying Trainable and Non-Trainable Parameters in Batch Normalization Batch normalization (BN) is a powerful technique used in deep learning to stabilize and accelerate training. The core idea behind BN is to normalize the output of a previous layer by subtracting the batch mean and dividing by the batch standard deviation. This is expressed by the following general formula: \[\hat{x} = \frac{x – \mu_B}{\sqrt{\sigma_B^2 + \epsilon}}\]\[y = \gamma \hat{x} + \beta\] Where: Why This Formula is Helpful The normalization step ensures that the input to each layer has a consistent distribution, which addresses the problem of “internal covariate shift”—where the distribution of inputs to a layer changes during training. By maintaining a stable distribution, the training process becomes more efficient, requiring less careful initialization of parameters and allowing for higher learning rates. The addition of \( \gamma \) and \( \beta \) parameters allows the model to restore the capacity of the network to represent the original data distribution. This means that the model can learn any representation it could without normalization, but with the added benefits of stabilized and accelerated training. The use of batch normalization has been shown empirically to result in faster convergence and improved model performance, particularly...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
45

Batch normalisation part 2 – day 26

Introduction to Batch Normalization Batch normalization is a widely used technique in deep learning that significantly improves the performance and stability of neural networks. Introduced by Sergey Ioffe and Christian Szegedy in 2015, this technique addresses the issues of vanishing and exploding gradients that can occur during training, particularly in deep networks. Why Batch Normalization? In deep learning, as data propagates through the layers of a neural network, it can lead to shifts in the distribution of inputs to layers deeper in the network—a phenomenon known as internal covariate shift. This shift can cause issues such as vanishing gradients, where gradients become too small, slowing down the training process, or exploding gradients, where they become too large, leading to unstable training. Traditional solutions like careful initialization and lower learning rates help, but they don’t entirely solve these problems. What is Batch Normalization? Batch normalization (BN) mitigates these issues by normalizing the inputs of each layer within a mini-batch, ensuring that the inputs to a given layer have a consistent distribution. This normalization happens just before or after the activation function of each hidden layer. Here’s a step-by-step breakdown of how batch normalization works: Zero-Centering and Normalization: \[ \mu_B = \frac{1}{m_B}...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
24

Batch Normalization – day 25

Understanding Batch Normalization in Deep Learning Understanding Batch Normalization in Deep Learning Deep learning has revolutionized numerous fields, from computer vision to natural language processing. However, training deep neural networks can be challenging due to issues like unstable gradients. In particular, gradients can either explode (grow too large) or vanish (shrink too small) as they propagate through the network. This instability can slow down or completely halt the learning process. To address this, a powerful technique called Batch Normalization was introduced. The Problem: Unstable Gradients In deep networks, the issue of unstable gradients becomes more pronounced as the network depth increases. When gradients vanish, the learning process becomes very slow, as the model parameters are updated minimally. Conversely, when gradients explode, the model parameters may be updated too drastically, causing the learning process to diverge. Introducing Batch Normalization Batch Normalization (BN) is a technique designed to stabilize the learning process by normalizing the inputs to each layer within the network. Proposed by Sergey Ioffe and Christian Szegedy in 2015, this method has become a cornerstone in training deep neural networks effectively. How Batch Normalization Works Step 1: Compute the Mean and Variance For each mini-batch of data, Batch Normalization first...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
17

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...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
19

How Create API by Deep Learning to Earn Money and what is the Best Way for Mac Users – Breaking studies on Day 22

How to Make Money by Creating APIs for Deep Learning – Part 1 Creating APIs (Application Programming Interfaces) for deep learning presents numerous opportunities to monetize your skills and knowledge in the rapidly expanding field of artificial intelligence (AI). Whether you’re an individual developer or a business, offering APIs that leverage deep learning models can be a lucrative venture. Here’s a detailed guide on how to capitalize on this opportunity. 1. Understanding the Value of Deep Learning APIs Deep learning APIs provide a way to expose powerful machine learning models to other applications or developers, enabling them to integrate complex functionalities without building models from scratch. For example, APIs for image recognition, natural language processing, or recommendation systems are in high demand across various industries. These APIs allow businesses to: Automate complex tasks such as sentiment analysis, object detection, or predictive analytics. Enhance their products with AI-driven features like personalized recommendations or automated customer service. Save time and resources by using pre-built models rather than developing their own from scratch. 2. Monetization Strategiesa. Subscription-Based Model How It Works: Charge users a recurring fee for access to your API. This could be based on usage (e.g., number of API calls) or...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
58

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:...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
77

Mastering Hyperparameter Tuning & Neural Network Architectures: Exploring Bayesian Optimization_ Day 19

In conclusion, Bayesian optimization does not change the internal structure of the model—things like the number of layers, the activation functions, or the gradients. Instead, it focuses on external hyperparameters. These are settings that control how the model behaves during training and how it processes the data, but they are not part of the model’s architecture itself. For instance, in this code, Bayesian optimization adjusts: So, while the model’s internal structure—like layers and activations—remains unchanged, Bayesian optimization helps you choose the best external hyperparameters. This results in a better-performing model without needing to re-architect or directly modify the model’s components....

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
9

Automatic vs Manual optimisation in Keras_. day 18

First check automatic – keras tuner – which is explained in our previous post Automated Hyperparameter Tuning in Keras Part 1: Automated Approaches for Hyperparameter Tuning in Keras Hyperparameter tuning is a crucial step in machine learning that involves finding the best set of parameters for your model to optimize its performance. Keras provides a robust toolset for this purpose through its KerasTuner library, which offers several powerful, automated methods to explore the hyperparameter space. In this section, we’ll dive into the different models and approaches available in Keras for automated hyperparameter tuning, updated with the latest in 2024. 1. Random Search Random search is one of the simplest and most straightforward hyperparameter tuning methods. It works by randomly sampling hyperparameter combinations from the predefined search space. Despite its simplicity, random search can be surprisingly effective, especially when combined with a well-chosen search space. It’s often used as a baseline method due to its ease of implementation and ability to explore diverse regions of the hyperparameter space. tuner = kt.RandomSearch( build_model, objective='val_accuracy', max_trials=10, executions_per_trial=2, directory='random_search_dir', project_name='random_search' ) tuner.search(x_train, y_train, epochs=5, validation_data=(x_val, y_val)) Here, max_trials defines the number of different hyperparameter combinations to try, while executions_per_trial allows for multiple runs to...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
30

Hyperparameter Tuning with Keras Tuner _ Day 17

A Comprehensive Guide to Hyperparameter Tuning with Keras Tuner Introduction In the world of machine learning, the performance of your model can heavily depend on the choice of hyperparameters. Hyperparameter tuning, the process of finding the optimal settings for these parameters, can be time-consuming and complex. This guide will walk you through the essentials of hyperparameter tuning using Keras Tuner, helping you build more efficient and effective models. Why Hyperparameter Tuning Matters Hyperparameters are critical settings that can influence the performance of your machine learning models. These include the learning rate, the number of layers in a neural network, the number of neurons per layer, and many more. Finding the right combination of these settings can dramatically improve your model’s accuracy and efficiency. Introducing Keras Tuner Keras Tuner is an open-source library that provides a streamlined approach to hyperparameter tuning for Keras models. It supports various search algorithms, including random search, Hyperband, and Bayesian optimization. This tool not only saves time but also ensures a systematic exploration of the hyperparameter space. Step-by-Step Guide to Using Keras Tuner 1. Define Your Model with Hyperparameters Begin by defining a model-building function that includes hyperparameters: import keras_tuner as kt import tensorflow as tf...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
56

TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras _. day 16

Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras TensorFlow and Keras provide powerful tools for building, training, and evaluating deep learning models. In this blog post, we will explore three essential techniques: Using TensorBoard for visualization Utilizing callbacks to enhance model training Saving and restoring models Using TensorBoard for Visualization TensorBoard is an interactive visualization tool that helps you understand your model’s training dynamics. It allows you to view learning curves, compare metrics between multiple runs, and analyze training statistics. Installation !pip install -q -U tensorflow tensorboard-plugin-profile Setting Up Logging Directory We need a directory to save our logs. This directory will contain event files that TensorBoard reads to visualize the training process. from pathlib import Path from time import strftime def get_run_logdir(root_logdir="my_logs"): return Path(root_logdir) / strftime("run_%Y_%m_%d_%H_%M_%S") run_logdir = get_run_logdir() Saving and Restoring a Model Keras allows you to save the entire model (architecture, weights, and training configuration) to a single file or a folder. Saving a Model model.save("my_keras_model", save_format="tf") Loading a Model model = tf.keras.models.load_model("my_keras_model") Saving Weights Only model.save_weights("my_weights.h5") model.load_weights("my_weights.h5") Using Callbacks Callbacks in Keras allow you to perform actions at various stages of training (e.g., saving...

Membership Required

You must be a member to access this content.

View Membership Levels

Already a member? Log in here
Read More
15