Artificial Intelligence – Ghazi Hudeihed

Tag Archives: Artificial Intelligence

Enhancing Neural Network Performance with Dropout Techniques

Introduction In the field of machine learning, neural networks are highly effective, excelling in tasks like image recognition and natural language processing. However, these powerful models often face a significant challenge: overfitting. Overfitting is akin to training a student only with past exam questions – they perform well on those specific questions but struggle with…

June 9, 2024 Ghazi Hudeihed

AI Artificial Intelligence coding example Data Science Deep Learning DropBlock Dropout hands-on guide Machine Learning MNIST Model Generalization Neural Networks NumPy Overfitting PyTorch Regularization Stochastic Depth Vanishing Gradients

Blog, Machine Learning

Demystifying Neural Networks: Architectures, Implementations, and Applications

Introduction Overview of Neural Networks Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes, or neurons, organized in layers. These neurons process and transmit information, allowing neural networks to learn patterns and relationships within data. Importance and Applications Neural networks have become fundamental to…

June 6, 2024 Ghazi Hudeihed

Adagrad Adam Optimizer Artificial Intelligence Attention Mechanisms backpropagation Batch Normalization Chain Rule Convolutional Neural Networks Data Bias Dropout Dynamic Computation Graph Eager Execution Explainable AI Feedforward Neural Networks Gradient Descent Image Recognition L1 Regularization L2 Regularization Machine Learning Multi-Head Attention Natural Language Processing Neural Networks Optimization Algorithms PyTorch Recommendation Systems Recurrent Neural Networks Regularization Techniques RMSprop Self-Attention Speech Recognition TensorFlow Training Algorithms Transfer Learning Transformer Networks

Blog, Machine Learning

Optimizing Machine Learning Models with Effective Regularization Techniques

Introduction Regularization techniques are essential in machine learning to prevent overfitting and improve the generalization of models. These techniques add constraints or penalties to the model to reduce its complexity. In this blog, we will explore various regularization methods, their mathematical definitions, and their effects during the forward and backward passes. L1 and L2 Regularization…

May 31, 2024 Ghazi Hudeihed

Artificial Intelligence Batch Normalization CutMix Data Augmentation Deep Learning DropConnect Dropout Early Stopping Elastic Net Gradient Descent L1 Regularization L2 Regularization Label Smoothing Lasso Layer Normalization Loss Function Machine Learning Max Norm Max Norm Regularization MixUp ML Optimization Model Generalization Neural Networks Overfitting Regularization Ridge Stochastic Depth Weight Decay

Tag Archives: Artificial Intelligence

Enhancing Neural Network Performance with Dropout Techniques

Demystifying Neural Networks: Architectures, Implementations, and Applications

Optimizing Machine Learning Models with Effective Regularization Techniques

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