Deep Learning Models integration for iOS Apps – briefly explained – Day 52
Key Deep Learning Models for iOS Apps Natural Language Processing (NLP) Models NLP models enable apps to understand and generate human-like text, supporting features like […]
Deep Learning Examples, Short OverView – Day 51
Comprehensive Guide to Deep Learning in 2024 and 2025: Trends, Types, and Beginner Tips Deep learning continues to be at the forefront of advancements in […]
Deep Neural Networks vs Dense Network – Day 50
Deep Neural Networks (DNNs) vs Dense Networks Understanding the distinction between Deep Neural Networks (DNNs) and Dense Networks is crucial for selecting the appropriate architecture […]
Learn Max-Norm Regularization to avoid overfitting : Theory and Importance in Deep Learning and proof – Day 49
Max-Norm Regularization: Theory and Importance in Deep Learning Introduction Max-norm regularization is a weight constraint technique used in deep learning to prevent the weights of […]
DropOut and Monte Carlo Dropout (MC Dropout)- Day 48
Understanding Dropout in Neural Networks with a Real Numerical Example In deep learning, overfitting is a common problem where a model performs extremely well on […]
Understanding Regularization in Deep Learning – Day 47
Understanding Regularization in Deep Learning – A Mathematical and Practical Approach Introduction One of the most compelling challenges in machine learning, particularly with deep learning […]
Comparing TensorFlow (Keras), PyTorch, & MLX – Day 46
Comparing Deep Learning on TensorFlow (Keras), PyTorch, and Apple’s MLX Deep learning frameworks such as TensorFlow (Keras), PyTorch, and Apple’s MLX offer powerful tools to […]
Learning Rate – 1-Cycle Scheduling, exponential decay and Cyclic Exponential Decay (CED) – Part 4 – Day 45
Advanced Learning Rate Scheduling Methods for Machine Learning: Learning rate scheduling is critical in optimizing machine learning models, helping them converge faster and avoid pitfalls […]
Exploring Gradient Clipping & Weight Initialization in Deep Learning – Day 44
Understanding Gradient Clipping and Weight Initialization Techniques in Deep Learning In this part, we explore the fundamental techniques of gradient clipping and weight initialization in […]
Theory Behind 1Cycle Learning Rate Scheduling & Learning Rate Schedules – Day 43
The 1Cycle Learning Rate Policy: Accelerating Model Training In our pervious article (day 42) , we have explained The Power of Learning Rates in […]
