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Transfer Learning in Machine Learning
Transfer learning is a technique in machine learning where a model developed for one task is reused as the starting point for a model on a second task. Rather than training a model entirely from scratch, which often requires large amounts of labeled data and computational resources, transfer learning enables a more efficient approach by leveraging previously learned features
The 'Reinforcement Learning Playground App' is an educational and interactive tool designed to help users understand the fundamentals of reinforcement learning, specifically through...
Training a Neural Network
Training is the process of teaching a neural network how to make predictions by providing it with data and allowing it to learn from that data.
Keras Overview
[..]a high-level deep learning library designed to simplify the creation of neural networks while providing the flexibility needed for advanced applications[..]
The 'Reinforcement Learning Playground App' is an educational and interactive tool designed to help users understand the fundamentals of reinforcement learning...
Dynamic Neural Network Animation App
[..]Our app offers dynamic visualizations of neural networks, allowing users to see how data flows through different layers[..]
Dynamic Pricing with Machine Learning
[..]refers to the practice of adjusting product or service prices in response to changing conditions. This could include shifts in demand, customer behavior, market trends, or even the time of day. While the concept has existed for decades—most notably in the airline and hospitality sectors—machine learning has brought a new level of precision and scale to its execution[..]
Machine Learning Terminology
[..]As the field continues to grow and evolve, it has introduced a rich lexicon of terms and concepts that are essential for understanding and applying machine learning techniques effectively[..]
Machine Learning Libraries: TensorFlow, PyTorch & scikit-learn
[..]behind every successful machine learning model lies a robust library or framework that simplifies the process of building, training, and deploying these models[..]
Policy Gradient Methods
Policy gradient methods are a class of reinforcement learning algorithms designed to optimize policies directly by maximizing expected cumulative rewards. Unlike value-based methods that estimate the value function and derive policies from it, policy gradient methods learn...
ML&NN articles:
°Applications of GANs and RNNs in Composing Music and Sound Generation
https://ml-nn.eu/a1/56.html
°Ray: A Framework for Distributed Computing
https://ml-nn.eu/a1/74.html
°Dynamic Pricing with Machine Learning
https://ml-nn.eu/a1/84.html
Quantum Neural Networks
[..]QNNs promise to overcome limitations inherent in classical computing, offering transformative potential across various domains such as cryptography, optimization, drug discovery, and artificial intelligence[..]
This project aims to analyze and visualize crime data in Bucharest using geospatial mapping and machine learning techniques. The primary focus is on classifying crime severity and making predictions about...
Handling Missing Data in Machine Learning
Missing data is a common issue in machine learning that can significantly impact the performance of models if not handled properly. Missing data can occur due to various reasons, such as data entry errors, equipment malfunctions, or non-responses in surveys. The way missing data is managed can influence the accuracy, efficiency, and robustness of machine learning models.
Optimizing Neural Networks
Optimizing neural networks involves improving how the model learns, generalizes, and operates under different conditions. This article offers a detailed exploration of the key techniques for optimizing neural networks, from...
In this project, we create a miniature version of a Large Language Model (LLM) using Python. The goal is to build a conversational AI that can answer user questions based on a predefined knowledge base stored in a JSON file.
Datasets typically consist of a collection of related sets of data compiled from various sources such as public records, research studies, or crowd-sourced information. These datasets enable ML practitioners to train algorithms in recognizing specific patterns or making predictions, thereby improving the overall performance of their models.
Below is a list of the top websites that provide datasets...
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https://ml-nn.eu/articles.html
Horovod is an open-source library designed for distributed training of deep learning models across multiple GPUs and nodes. It is framework-agnostic and integrates seamlessly with popular machine learning libraries such as TensorFlow, PyTorch, Keras...
Machine Learning Libraries: TensorFlow, PyTorch & scikit-learn
[..]behind every successful machine learning model lies a robust library or framework that simplifies the process of building, training, and deploying[..]