In this video, we demonstrate how to use MATLAB and MQL4 programming languages to forecast the price of gold in the forex market. We'll walk you through the process of time series analysis, which involves analyzing and modeling patterns in historical price data to make predictions about future trends.
https://www.youtube.com/watch?v=7zSKoqd1LXs

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⚜️Neural network course session one::
1️⃣Introduction to Neural Networks

🔵This video provides an introduction to the fascinating world of neural networks. We explore the biological inspiration behind artificial neural networks, drawing parallels between the human brain and these computational models. Key topics covered include:

✅History of neural networks and major milestones
✅Comparison of biological and artificial neuron speeds
✅Loss of neurons with age and neuroplasticity
✅How the brain processes information and learns
✅Applications of neural networks across diverse fields
✅Further reading resources on neural network fundamentals
To see the next meeting earlier, visit the YouTube
🔻YouTube: second session
https://youtu.be/JtBebQ2CJKs

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⚜️Neural network course session two::
2️⃣Neuron Model and Network

🔵Explore neuron models and neural network architectures in this comprehensive session. Understand the mathematical foundations of these computational models. Study single and multiple-input neuron models, transfer functions, and how neurons form network building blocks. Discover single-layer, multi-layer, and recurrent network architectures designed for various problem complexities. Learn about feedback loops enabling temporal behavior in recurrent networks.

✅Neuron Model
✅Transfer Functions
✅Network Architectures
✅Recurrent Networks
🔻YouTube: third session
https://youtu.be/DvaMtUP095Q
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⚜️Neural network course session four::
4️⃣Perceptron Learning Rule

🔵In this MATLAB tutorial video, we dive into the fundamentals of the Perceptron Learning Rule, a powerful algorithm for training single-layer neural networks. Through practical examples and step-by-step explanations, you'll learn how to implement the Perceptron Learning Rule in MATLAB to solve linearly separable classification problems.
We cover key concepts such as:

✅Perceptron architecture and decision boundaries
✅Supervised learning and training sets
✅Weight and bias updates using the Perceptron Learning Rule
✅Convergence and limitations of the Perceptron network
🔻YouTube: third session

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✳️ Deep Network Designer in MATLAB - Quick Guide

🔰 In this tutorial, you’ll learn how to use MATLAB's Deep Network Designer to build and train deep neural networks effortlessly. Whether you're a beginner or advanced user, this step-by-step guide will help you design custom networks, import pre-trained models, adjust layers and hyperparameters, and train/evaluate your models with ease.

Produced by Saeed Heibati and Amirhossein Jalali, with consulting by Naser Pakar.

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