4.6
Beginner
6h
Updated 2 months ago
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.
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.
Machine learning is one of the fastest growing fields, and we cannot emphasize enough about its importance. This course aims to ...Show More
Content
1.
Prologue
3 Lessons
Get familiar with AI's evolution, neural networks, and building your own in Python.
2.
A Little Background
10 Lessons
Grasp the fundamentals of neural network basics, prediction models, classification, and training processes.
Humans vs. ComputersA Simple Prediction MachineEstimate the Constant IterativelyClassify vs. PredictBuild a Simple ClassifierErrors in the Training ClassifierRefine the Parameters of the Training ClassifierSet Up a Learning Rate in the Training ClassifierLimitations of Linear ClassifiersRepresent Boolean Functions with Linear Classification
3.
Let's Get Started!
11 Lessons
Explore the foundational concepts of neural networks, from biological neurons to matrix calculations.
4.
Backward Propagation of Error
5 Lessons
Break down the steps to efficient error correction using backpropagation in neural networks.
5.
Adjusting the Link Weights
10 Lessons
Dig into updating link weights, gradient descent optimization, and preparing neural network data.
6.
A Gentle Start with Python
7 Lessons
Simplify complex topics in Python, loops, functions, arrays, plotting, objects, and methods.
7.
Neural Network with Python
8 Lessons
Learn how to improve your skills in building, training, and initializing neural networks in Python.
8.
Testing Neural Network against MNIST Dataset
11 Lessons
Step through evaluating and testing your neural network using the MNIST dataset.
9.
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 Lessons
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.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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