Summary of Artificial Neural Network and Deep Learning: Fundamentals and Theory, by M. M. Hammad
Artificial Neural Network and Deep Learning: Fundamentals and Theory
by M. M. Hammad
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper offers a comprehensive exploration of the foundational principles and advanced methodologies in neural networks and deep learning. The text begins by laying a solid groundwork for understanding data and probability distributions using concepts from descriptive statistics and probability theory. It then delves into matrix calculus and gradient optimization, which are crucial for training and fine-tuning neural networks. The paper explores multilayer feed-forward neural networks, explaining their architecture, training processes, and the backpropagation algorithm. Key challenges in neural network optimization, such as activation function saturation, vanishing and exploding gradients, and weight initialization, are thoroughly discussed. The text covers various learning rate schedules and adaptive algorithms, providing strategies to optimize the training process. Techniques for generalization and hyperparameter tuning, including Bayesian optimization and Gaussian processes, are presented to enhance model performance and prevent overfitting. Advanced activation functions are explored in detail, categorized into sigmoid-based, ReLU-based, ELU-based, miscellaneous, non-standard, and combined types. Each activation function is examined for its properties and applications, offering readers a deep understanding of their impact on neural network behavior. The final chapter introduces complex-valued neural networks, discussing complex numbers, functions, and visualizations, as well as complex calculus and backpropagation algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This book teaches you about artificial intelligence and how it works. It starts by explaining the basics of data and probability. Then, it goes into more advanced ideas like matrix calculations and gradient optimization. The book covers different types of neural networks and how they’re trained. It also talks about challenges that come up when training these networks, like getting stuck or making mistakes. To solve these problems, the book shows you various ways to optimize the training process. It also explains how to make sure your model is good at generalizing what it’s learned and not just memorizing data. The book even covers some special types of neural networks that use complex numbers! This book will help you understand how to design and optimize these advanced models, which can contribute to new advances in artificial intelligence. |
Keywords
» Artificial intelligence » Backpropagation » Deep learning » Fine tuning » Generalization » Hyperparameter » Neural network » Optimization » Overfitting » Probability » Relu » Sigmoid
