Discover the basics of Machine Learning with our beginner-friendly guide. Dive into basic concepts, algorithms, and real-world applications, paving the way for data-driven insights and innovation
In this post, we'll explore the fundamental concepts of dataset splitting in machine learning. We'll cover the definitions of train, validation, and test sets, the importance of splitting the dataset, different partitioning strategies, and tips for ensuring proper dataset splitting. Join us as we unravel the keys to effective model development and evaluation.
In this post, we'll explore how to prevent overfitting in your machine learning models using simple regularization techniques. Dive into controlling model complexity and improving generalization for better performance in real-world situations.
Overfitting, underfitting, and a model's capacity are critical concepts in deep learning, particularly in the context of training neural networks. In this post, we'll learn how a model's capacity leads to overfitting and underfitting of the data and its effect on the performance of a neural network. Let's begin! Overview OverviewModel's CapacityGeneralization GapHow Model's Capacity Affects the Way A Model Fits the Same Set of DataHow to Know If The Model Would Work Well on Unseen Data?SummaryFurther Reading In this post, you will learn: What is the model's capacity? How model's capacity affect the way the model fits the same set of data? Concept of Overfitting, underfitting, and finding just the right fit How to know if the model would work well on unseen data? Model's Capacity A model's capacity refers to its ability to capture and represent complex patterns in the data. It reflects the flexibility and complexity of the model architecture. Let's understand this with the help of an example: We can train a model using historical data and make predictions about the lottery based on that trained model as shown in the figure below: Figure 1: ML model to predict lottery The problem is that the model being able to fit on seen data, doesn't mean that it will perform well on unseen data. This means that the model with high capacity (has a large number of parameters and is trained long enough) can memorize training samples. In the lottery case, the input can be the date of the lottery and the output can be the lucky number of that day's lottery. If we train a…
Loss Functions - Training a neural network is an optimization problem. The goal is to find parameters that minimize this loss function and increase the model's performance as a consequence. So, training a neural network means finding the weights that minimize our loss function. This means that we need to know what loss functions are to make sure to use the right one based on the neural network we are training to solve a particular problem. We will learn what loss functions are, what type of loss functions to use for a given problem, and how they impact the output of the neural network. Let's begin. OverviewLoss FunctionsWhat is a Loss Function?How Do Loss Functions Work?Which Loss Functions To Use for Regression and ClassificationLoss Functions for RegressionLoss Functions for Classification SummaryFurther ReadingRelated ArticlesRelated Videos Loss Functions What is a Loss Function? Loss functions, also known as error functions , indicate how well the model is performing on the training data, allowing for the updating of weights towards reducing the loss, thereby enhancing the neural network's performance. In other words, the loss function acts as a guide for the learning process within a machine learning algorithm or a neural network. It quantifies how well the model's predictions match the actual target values during training. Here are some terminology that you should be familiar with regarding calculating this. Loss Function: Applied to a single training example and measures the discrepancy between the predicted output and the true target. Cost Function: Refers to the aggregate (sum) of loss function over the entire dataset, including any regularization terms. Objective Function: This term is…
Learn how Rectified Linear Unit (ReLU) activation functions can revolutionize your deep neural network training. Discover how ReLU prevents gradients from vanishing, tackles the issue of dying neurons, and explore advanced techniques for optimal performance. Dive into the world of ReLU and unlock the full potential of your neural network models
The exploding gradients occur in a situation opposite to the vanishing gradient problem. Instead of gradients becoming vanishingly small, they become extremely large during training. This makes your model unstable and unable to learn from your training data. In this post, we will understand the problem of exploding gradients in deep artificial neural networks. Let's begin Overview In this post, we will cover: What exploding gradient is and its causes. How do we know if the model has an exploding gradient problem? How to fix the exploding gradient problem? 1 - What are Exploding Gradients? The exploding gradient problem happens when the gradients in a neural network become so large that it messes up the training process. During backpropagation, the gradient of the loss function w.r.t. network's parameters (such as weights and biases) becomes extremely large. When the gradient becomes too large, it can lead to numerical instability and difficulties in training the neural network effectively. Essentially, the updates to the parameter become so large that they cause the network's parameter to "explode" meaning they grow uncontrollably. This can result in unpredictable behavior during training, making it difficult for the network to converge to a solution and hindering its ability to learn meaningful patterns in the data. 2 - Understanding Exploding Gradients Through Example Let's take the same example that we looked at for vanishing gradient problem, and see what exploding gradients would look like: Figure 1: Neural Network to predict if the person will take insurance or not For example, if we try to calculate the gradient of loss w.r.t. weight , where d1 = and d2 is…
Discover the common challenge of vanishing gradients in artificial neural networks and how it impacts the effectiveness of deep learning models. Dive into the root causes of this issue, explore the role of activation functions like sigmoid and tanh, and learn why deep neural networks are particularly susceptible. Uncover strategies to mitigate the vanishing gradient problem and optimize your neural network configurations for improved performance
Discover the power of neural networks, complex models inspired by the brain's design. While single-parameter networks give a basic grasp, real-world tasks need networks with many parameters. Follow along as we simplify the training process, exploring forward and backward steps, using math tricks. Learn practical tips for data prep and model setup to train neural networks effectively.
Learn how to train a neural network with just one parameter, exploring the basics of optimization. Dive into the process of fine-tuning this single parameter to improve model performance and achieve your goals effectively.
Discover the essence of deep learning optimization algorithms, ranging from the foundational Gradient Descent to the advanced strategies of AdaGrad and Adam. Dive into their intricacies and learn how they shape the training process, leading to optimal model performance and convergence.