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Make Your Own Neural Network in Python

Name: Make Your Own Neural Network in Python
Price: 59 USD
Rating: 4.72 (49 reviews)
Author: Tariq Rashid

Gain insights into building and optimizing neural networks in Python. Delve into fundamental concepts, mathematical explanations, and practical implementations to enhance your machine learning skills.

4.7

83 lessons

Created by Tariq Rashid

What's included

83 lessons across 12 modules
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Identify key milestones and motivations behind AI and neural networks
Differentiate prediction vs classification and build a simple linear classifier
Calculate forward-pass outputs using weights, sigmoid activation, and matrix multiplication
Implement backpropagation with matrix operations to compute errors and update weights
Apply gradient descent with squared error and learning rate to optimize network weights
Build, train, and query a three-layer neural network class in Python using NumPy/SciPy
Prepare, visualize, and evaluate MNIST data, including scaling, targets, and accuracy

Learning Roadmap

83 Lessons

Prologue

Get familiar with AI's evolution, neural networks, and building your own in Python.

The Search for Intelligent Machines

A Nature Inspired New Golden Age

Introduction

A Little Background

Grasp the fundamentals of neural network basics, prediction models, classification, and training processes.

Humans vs. Computers

A Simple Prediction Machine

Estimate the Constant Iteratively

Classify vs. Predict

Build a Simple Classifier

Errors in the Training Classifier

Refine the Parameters of the Training Classifier

Set Up a Learning Rate in the Training Classifier

Limitations of Linear Classifiers

Represent Boolean Functions with Linear Classification

Let's Get Started!

11 Lessons

Explore the foundational concepts of neural networks, from biological neurons to matrix calculations.

Backward Propagation of Error

5 Lessons

Break down the steps to efficient error correction using backpropagation in neural networks.

Adjusting the Link Weights

10 Lessons

Dig into updating link weights, gradient descent optimization, and preparing neural network data.

A Gentle Start with Python

7 Lessons

Simplify complex topics in Python, loops, functions, arrays, plotting, objects, and methods.

Neural Network with Python

8 Lessons

Learn how to improve your skills in building, training, and initializing neural networks in Python.

Testing Neural Network against MNIST Dataset

11 Lessons

Step through evaluating and testing your neural network using the MNIST dataset.

Some Suggested Improvements

4 Lessons

Unpack the core of optimizing neural network performance through learning rate, epochs, and structure adjustments.

10.

Even More Fun!

4 Lessons

Examine neural networks' adaptability with personal handwriting, internal visualization, and data augmentation through rotations.

11.

Epilogue

Grasp the fundamentals of neural networks and their impact on AI advancements.

Conclusion

12.

Appendix: A Small Guide to Calculus

9 Lessons

Dig into core calculus concepts and their applications in understanding variable changes.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

Machine learning is one of the fastest growing fields, and we cannot emphasize enough about its importance. This course aims to teach one of the fundamental concepts of machine learning, i.e., Neural Network. You will learn the basic concepts of building a model as well as the mathematical explanation behind Neural Network and based on that; you will build one from scratch (in Python). You will also learn how to train and optimize your network to achieve a better result.

ABOUT THE AUTHOR

Tariq Rashid

Art - Science - Computing - Teaching - Community

Learn more about Tariq

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