Building Neural Networks from Scratch with Python
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English Pages 174 Year 2023
Table of contents :
Title Page
Copyright
Contents
Introduction - Building Neural Networks from Scratch with Python
1. Introduction to Neural Networks
Neural Networks and Their Real-World Applications
Basic Building Blocks Of Neural Networks
Understanding The Structure and Components Of A Neural Network
2. Foundations of Neural Networks
Understanding the Core Mathematics of Neural Networks
Introducing Gradient Descent and Backpropagation Algorithms
Exploring Weight and Bias Updates
Overview of common loss functions and optimization techniques
3. Implementing Neural Networks In Python
Setting Up The Python Environment For Neural Network Development
Implementation Of A Basic Feedforward Neural Network
Addressing Common Coding Challenges
Testing And Evaluating Neural Network Models
4. Handling Complex Concepts in Neural Networks
Explaining Advanced Neural Network Architectures
Introducing Regularization Techniques
L1 And L2 Regularization for Weight Decay
Tackling Overfitting, Underfitting, and Model Capacity
5. Preparing Data For Neural Networks
Understanding The Significance Of Data Preprocessing In Neural Network Training
Simplifying Data Cleaning, Normalization, And Feature Scaling Techniques For Beginners
6. Making Neural Networks Interpretable
Addressing The Challenges Of Interpreting Neural Network Decisions For Beginners
Introducing Visualization Techniques For Understanding Model Behavior
Plotting Learning Curves And Loss Landscapes
Feature Importance and Saliency Maps
LIME, SHAP, and Integrated Gradients
References
7. Staying Updated With Neural Network Advancements
Navigating The Rapidly Evolving Field Of Neural Networks
Beginner-Friendly Resources For Staying Update
References
8. Putting It All Together: Beginner-Friendly Projects
Applying Neural Networks To Beginner-Friendly Real-World Projects
Practical Challenges And Tips for Deploying Models
References
Conclusion
References
Title Page
Copyright
Contents
Introduction - Building Neural Networks from Scratch with Python
1. Introduction to Neural Networks
Neural Networks and Their Real-World Applications
Basic Building Blocks Of Neural Networks
Understanding The Structure and Components Of A Neural Network
2. Foundations of Neural Networks
Understanding the Core Mathematics of Neural Networks
Introducing Gradient Descent and Backpropagation Algorithms
Exploring Weight and Bias Updates
Overview of common loss functions and optimization techniques
3. Implementing Neural Networks In Python
Setting Up The Python Environment For Neural Network Development
Implementation Of A Basic Feedforward Neural Network
Addressing Common Coding Challenges
Testing And Evaluating Neural Network Models
4. Handling Complex Concepts in Neural Networks
Explaining Advanced Neural Network Architectures
Introducing Regularization Techniques
L1 And L2 Regularization for Weight Decay
Tackling Overfitting, Underfitting, and Model Capacity
5. Preparing Data For Neural Networks
Understanding The Significance Of Data Preprocessing In Neural Network Training
Simplifying Data Cleaning, Normalization, And Feature Scaling Techniques For Beginners
6. Making Neural Networks Interpretable
Addressing The Challenges Of Interpreting Neural Network Decisions For Beginners
Introducing Visualization Techniques For Understanding Model Behavior
Plotting Learning Curves And Loss Landscapes
Feature Importance and Saliency Maps
LIME, SHAP, and Integrated Gradients
References
7. Staying Updated With Neural Network Advancements
Navigating The Rapidly Evolving Field Of Neural Networks
Beginner-Friendly Resources For Staying Update
References
8. Putting It All Together: Beginner-Friendly Projects
Applying Neural Networks To Beginner-Friendly Real-World Projects
Practical Challenges And Tips for Deploying Models
References
Conclusion
References
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- Algorithms and Data Structures: Pattern Recognition
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