HeyJaredArmin
Machine Learning | Deep Learning | Reinforcement Learning | Control Theory Sharing educational content, research insights, and tutorials on AI, data-driven systems, optimization, and intelligent control.
- Indexed videos, last 90 days
- 5
- Latest publication
- Aug 2, 2026
- Audience
- ~16 subscribers
- Earliest in this view
- Jul 4, 2026
Latest videos
Transfer Learning with VGG16: Feature Extraction for Cats vs Dogs Classification (opens the original)
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This video presents a practical implementation of Transfer Learning using VGG16 pre-trained on ImageNet. The convolutional base of VGG16 is used as a fixed feature extractor by setting include_top=False. The extracted feature representations are then used to train a task-specific classifier for binary image classification on the Cats vs Dogs dataset. The implementation covers: • Loading VGG16 with pre-trained ImageNet weights • Separating feature extraction from task-specific classification • Ex
Data Augmentation in Deep Learning | Reducing Overfitting in CNNs (opens the original)
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In this video, we explore one of the most effective techniques for improving image classification models: Data Augmentation. Using the Cats vs Dogs dataset, we apply random image transformations with ImageDataGenerator and compare the results with our previous CNN model. You'll see how data augmentation helps reduce overfitting and improves a model's ability to generalize to unseen data. Code: https://github.com/Armin-HassanzadehHassanabad/Deep-Learning/blob/main/CNNpart2.ipynb
Convolutional Neural Networks (CNN) Explained | Build an Image Classifier with TensorFlow (opens the original)
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In this video, we build a Convolutional Neural Network (CNN) for image classification using TensorFlow and Keras. We prepare the dataset, build the model, train it, analyze the results, and discuss overfitting and Dropout. #DeepLearning #CNN #TensorFlow #Keras #MachineLearning
Nonlinear System Identification (opens the original)
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This video introduces the fundamentals of Nonlinear System Identification and the Nonlinear ARX (NLARX) model. It explains how nonlinear regressors and nonlinear mappings extend the classical ARX framework to model complex dynamic systems, providing an intuitive understanding of the NLARX architecture and its role in modern system identification.
AI and Data-Driven Control Systems | Part 3 (opens the original)
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Modern control systems increasingly operate in environments characterized by uncertainty, nonlinear dynamics, and limited system knowledge. These challenges have driven the development of data-driven and AI-based control strategies that extend beyond traditional model-based approaches. This video presents an overview of five widely used control methodologies: • Active Disturbance Rejection Control (ADRC) • Model Reference Adaptive Control (MRAC) • Extremum Seeking Control (ESC) • Fuzzy Logic Con
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~16 subscribers
Measured Sep 19, 2026
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