Fundamentals of Neural Networks Series: Table of Contents

Introduction

The “Fundamentals of Neural Networks Series” is designed to gradually guide readers through the key concepts and techniques of neural networks. Each article focuses on a different core topic, ranging from the basic principles of neural networks to their applications in the real world, providing a comprehensive learning path for readers.

Table of Contents

Article 1: Introduction to Neural Networks

Title: Fundamentals of Neural Networks Series 1 — Demystifying Artificial Intelligence: An Introduction to Neural Networks

Content:

• The history and background of neural networks
• Basic terminology explained: neurons, weights, activation functions
• Example of a simple network: the single-layer perceptron
• The learning process of neural networks: basics of loss functions and backpropagation

Article 2: Multilayer Perceptron (MLP)

Title: Fundamentals of Neural Networks Series 2 — Building Intelligence: The Mysteries of Multilayer Perceptrons and Deep Learning

Content:

• From single layer to multilayer: introducing the concept of hidden layers
• The role and common types of activation functions: ReLU, Sigmoid, etc.
• How to build a basic MLP model
• Applications of MLP in solving real-world problems

Article 3: Feedforward Neural Networks

Title: Fundamentals of Neural Networks Series 3 — The Power of Data: Information Flow in Feedforward Neural Networks

Content:

• Architecture and characteristics of feedforward networks
• How data propagates forward in the network
• Basics of loss functions and optimizers
• Building and training a simple feedforward network: an example

Article 4: Training and Tuning Neural Networks

Title: Fundamentals of Neural Networks Series 4 — The Art of Intelligent Learning: Techniques and Challenges in Training Neural Networks

Content:

• Differences and importance of training, validation, and test sets
• Using backpropagation and gradient descent
• Basics of hyperparameter tuning: learning rate, batch size, etc.
• Strategies to avoid overfitting: regularization, Dropout, etc.

Article 5: Practical Applications of Neural Networks

Title: Fundamentals of Neural Networks Series 5 — From the Lab to the Real World: Diverse Applications of Feedforward Neural Networks

Content:

• Application cases of neural networks in various fields: image recognition, speech recognition, natural language processing, etc.
• Specific requirements of different applications on network architecture and parameters
• Future trends and potential developments in deep learning

Conclusion

Through this series, our goal is to provide readers with a solid foundation in neural network knowledge and lay the groundwork for more advanced topics. Whether you are an academic researcher, a practical application developer, or a general reader interested in artificial intelligence, this series of articles will be an indispensable learning resource.

Further Learning Resources:

• Recommended Reading: “Deep Learning” by Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Online Courses: Neural network and deep learning-related courses offered on Coursera and edX
• Practical Tools: Building and experimenting with neural networks using Python and TensorFlow