Building Neural Networks from Scratch with Python

✍ Scribed by Knowings, L.D.

• Publisher: Independently Published
• Year: 2023
• Tongue: English
• Leaves: 174
• Category: Library

✦ Synopsis

Ready to throw your hat into the AI and machine learning ring? Get started right here, right now!
Are you sick of these machine-learning guides that don’t really teach you anything?
Do you already know Python, but you’re looking to expand your horizons and skills with the language?
Do you want to dive into the amazing world of neural networks, but it just seems like it’s… not for you?
Artificial intelligence is progressing at a fantastic rate—every day, a new innovation hits the net, providing more and more opportunities for the advancement of society.
In your everyday life, your job, and even in your passion projects, learning how to code a neural network can be game-changing.
But it just seems… complicated. How do you learn everything that goes into such a complex topic without wanting to tear your own hair out?
Well, it just got easier.
Machine learning and neural networking don’t have to be complicated—with the right resources, you can successfully code your very own neural network from scratch, minimal experience needed!
In this all-encompassing guide to coding neural networks in Python, you’ll uncover everything you need to go from zero to hero—transforming how you code and the scope of your knowledge right before your eyes.
Here’s just a portion of what you will discover in this guide
A comprehensive look at what a neural network is – including why you would use one and the benefits of including them in your repertoire
All that pesky math dissuading you? Get right to the meat and potatoes of coding without all of those confusing equations getting you down
Become a debugging master with these tips for handling code problems, maximizing your efficiency as a coder, and testing the data within your code
Technological advancements galore! Learn how to keep up with all the latest trends in tech—and why doing so is important to you
What in the world are layers and gradients? Detailed explanations of complex topics that will demystify neural networks, once and for all
Dealing with underfitting, overfitting, and other oversights that many other resources overlook
Several beginner-friendly neural network projects to put your newfound knowledge to the test
And much more.
Imagine a world where machine learning is more accessible, where neural networks and other complex topics are available to people just like you—people with a passion. Allowing for such technological advancements is going to truly change our world.

✦ 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