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#neuralnetworks

6 posts6 participants1 post today
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Calin Sandu

Gradient Descent is one of the most fundamental optimization algorithms used in machine learning and deep learning. It helps minimize the cost function by iteratively adjusting model parameters to find the optimal solution. Imagine you are standing on the top of a hill, and your goal is to find the lowest point in the valley. In Gradient Descent, the cost function represents the terrain, and the algorithm helps..

ml-nn.eu/a1/65.html

ml-nn.euGradient Descent Explained with VisualizationsMachine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
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Calin Sandu

Hugging Face Transformers is a leading open-source library designed to simplify the use and development of transformer-based models, which have become foundational in natural language processing (NLP) and beyond. This library enables developers to leverage state-of-the-art models for tasks such as text classification, machine translation, question answering, and even computer vision and audio processing.

ml-nn.eu/a1/75.html

ml-nn.euHugging Face Transformers OverviewMachine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
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Calin Sandu

Markov Decision Processes

MDPs are mathematical frameworks used to model decision-making in environments where outcomes are partly random and partly under the control of a decision maker. They are widely used in various fields, including artificial intelligence, robotics, economics, and operations research, to optimize decisions over time.

ml-nn.eu/a1/49.html

ml-nn.euMarkov Decision Processes (MDPs)Machine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
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Calin Sandu

Pruning in Neural Networks: A Comprehensive Overview

Pruning is a critical technique in the field of neural networks, aimed at optimizing model performance, reducing computational complexity, and improving efficiency. As neural networks have grown in size and complexity, particularly with the advent of deep learning, the need for techniques to manage and streamline these models has become increasingly important.

ml-nn.eu/a1/77.html

ml-nn.euPruning in Neural Networks: A Comprehensive OverviewMachine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
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Calin Sandu

How LLMs Improve Language Translations

Language translation has always been a challenging task, requiring not just direct word-to-word conversions but also an understanding of context, tone, and cultural nuances. Traditional translation methods, including rule-based and statistical approaches, have struggled with these complexities. However, the advent of Large Language Models...

ml-nn.eu/a2/1.html

ml-nn.euHow LLMs Improve Language TranslationsMachine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
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Calin Sandu

Supervised Learning in Artificial Neural Networks

Artificial Neural Networks are a fundamental component of machine learning, mimicking the human brain's structure and functioning. Within ANNs, one of the key paradigms is supervised learning, a powerful approach that enables the network to learn from labeled training data. In this article, we'll delve into the concept of supervised learning within artificial...

ml-nn.eu/a1/3.html

ml-nn.euSupervised Learning in Artificial Neural NetworksMachine Learning & Neural Networks Blog
#Programming#MachineLearning#NeuralNetworks
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