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Welcome to Master Neural Networks: Build with JavaScript and React. This comprehensive course is designed for anyone looking to understand and build neural networks from the ground up using JavaScript and React.

What You'll Learn:

1. Introduction to Neural Networks

   • Understand the basics of perceptrons and their similarities to biological neurons.

   • Learn how perceptrons work at a fundamental level.

2. Building a Simple Perceptron

   • Code a perceptron to classify simple objects (e.g., pencils vs. erasers) using hardcoded data.

   • Implement a basic perceptron from scratch and train it with sample inputs and outputs.

   • Draw graphs and explain the steps needed, including defining weighted sums and activation functions.

3. Perceptron for Number Recognition

   • Advance to coding a perceptron for number recognition using the MNIST dataset to identify if a number is 0 or not.

   • Train the perceptron using the MNIST dataset, optimizing weights and biases.

   • Learn techniques to calculate accuracy and handle misclassified data.

   • Save and export the trained model for use in web applications.

4. Parsing and Preprocessing MNIST Data

   • Learn to parse and preprocess MNIST data yourself.

   • Understand the file formats and the steps needed to convert image data into a usable format for training.

5. Building a Multi-Layer Perceptron (MLP)

   • Develop a more complex MLP to recognize digits from 0 to 9.

   • Implement training algorithms and understand backpropagation.

   • Explore various activation functions like ReLU and Softmax.

6. Practical Implementation with JavaScript and React

   • Integrate neural networks into web applications using JavaScript, React, and Node.js.

   • Build and deploy full-stack applications featuring neural network capabilities.

   • Create a React application to test and visualize your models, including drawing on a canvas and making predictions.

7. Integrate TensorFlow library

   1. Learn to use TensorFlow

   2. Code Neural network to recognize numbers from 0-9

Course Features:

• Step-by-step coding tutorials with detailed explanations.

• Hands-on projects to solidify your understanding.

• Graphical visualization of neural network decision boundaries.

• Techniques to save and export trained models for real-world applications.

• Comprehensive coverage from basic perceptrons to multi-layer perceptrons.