The existing resources for GPT-2’s architecture are very good, but are written for researchers so I will provide you will a tailored concept map for all the areas you will need to know prior to jumping in.

A Friendly Introduction to Cross-Entropy for Machine Learning. Now, let's learn about Cross-Entropy, its extensions (Loss Function and KL Divergence) and their part in respect to Machine Learning.

In this article, I’ll walk through the mathematics behind Cycle-Consistent Adversarial Networks. Please read the paper for a more comprehensive explanation.

In this article, I will ask a few simple questions which I will then answer, hopefully after which you will not have any more mind-boggling questions about cross-entropy.

In this article, we will go through several loss functions and their applications in the domain of machine/deep learning.

Making Your Loss Function Count. Some errors are more costly than others; the way your model learns should reflect that.

How we improve delivery time estimation with a custom loss function.Dear connoisseurs, I invite you to take a look inside Careem’s food delivery platform. Specifically, we are going to look at how we use machine learning to improve the customer experience for delivery time tracking.

Face recognition using arcface present in Insightface.

Activation Functions, Optimization Techniques, and Loss Functions: A significant piece of a neural system Activation function is numerical conditions that decide the yield of a neural system.

Deep Learning is a subset of machine learning. Technically, machine learning looks through input data for valuable representations, making use of feedback signal as guidance

A simple example for training Gaussian actor networks. Defining a custom loss function and applying the GradientTape functionality, the actor network can be trained using only a few lines.

Numerical instability and weirdness of the softmax function. Once upon a time, I was trying to train a speaker recognition model with TIMIT dataset.

A guide to the most frequently used activation and loss functions, and a breakdown of their benefits and limitations. In this post, we’re going to discuss the most widely-used activation and loss functions for machine learning models.

A comprehensive yet simple approach to the basics of deep learning. The human brain is the most sophisticated of all supercomputers.

Loss functions are used to calculate the difference between the predicted output and the actual output. To know how they fit into neural networks, read :

For the calculation of Loss, various optimization techniques are used in the field of Machine learning and Deep learning. This article will cover commonly used loss function in Machine learning and Deep learning, its use and mathematics behind it.

Getting an essence of how loss is calculated in the great FaceNet Face recognition model.

Ever wondered how the machine learning algorithms give us the optimal result, whether it is prediction, classification or any other? How…