    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.

Make your own neutral network in python final.png

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.

Make Your Own Neural Network in Python

# Key takeaways
We've seen how some problems are easy for humans to solve, but hard for traditional computer approaches. Image recognition is one of these so-called artificial intelligence challenges.

Neural networks have enabled huge progress in image recognition, and a wide range of other kinds of hard problems too. A key part of scientists' early motivations was the puzzle that biological brains—like pigeon or insect brains—appeared to be simpler and slower than today's huge supercomputers, yet they could carry out complex tasks like flight, feeding, and building homes. Those biological brains also seemed extremely resilient to damage or imperfect signals. Digital computers and traditional computing weren't either of these things.

Today, neural networks are a key part of some of the successes in artificial intelligence. There is continued interest in neural networks and machine learning, especially deep learning, where a hierarchy of machine learning methods are used. In early 2016, Google's DeepMind beat a world master at the ancient game of Go. This is a massive milestone for artificial intelligence because Go requires much deeper strategy and nuance than chess. Researchers had thought that a computer playing that well was years away. Neural networks played a key role in that success.

We hope you've seen how the core ideas behind neural networks are quite simple. And we hope you've also had fun experimenting with neural networks. Perhaps we've sparked your interest in exploring other kinds of machine learning and artificial intelligence.

If we've done any of these things, we have succeeded.

A final comment on making our own neural network. 

Prologue

A Little Background

Let's Get Started!

Backward Propagation of Error

Adjusting the Link Weights

A Gentle Start with Python

Neural Network with Python

Testing Neural Network against MNIST Dataset

Some Suggested Improvements

Even More Fun!

Epilogue

Appendix: A Small Guide to Calculus

Conclusion

Key takeaways