Make Your Own Neural Network in Python

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.

Beginner

83 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

41 Playgrounds

This course includes

41 Playgrounds

Course Overview

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.

Course Content

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!

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

Neurons: Nature’s Computing Machines How Neurons Work Activation Functions Replicate Neurons in an Artificial Model Follow Signals through a Simple Network Calculate Neural Network Output Matrix Multiplication Calculate the Inputs for Internal Layers A Three-Layer Example: Working on the Input Layer A Three-Layer Example: Working on the Hidden Layer A Three-Layer Example: Working on the Output Layer

Backward Propagation of Error

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

Learn Weights from More than One Node Error Backpropagation from More Output Nodes Backpropagation: Splitting the Error Backpropagation: Recombine the Error Error Backpropagation with Matrix Multiplication

Adjusting the Link Weights

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

Update Weights Embrace Pessimism Gradient Descent Algorithm Transform the Output into an Error Function Use Gradient Descent to Update Weights Choose the Right Weights Iteratively The Error Slope between the Input and Hidden Layers Weight Updates Calculated Prepare Data: Inputs and Outputs Prepare Data: Random Initial Weights

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

1 Lesson

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

12.

Appendix: A Small Guide to Calculus

9 Lessons

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

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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