Master Neural Networks- Build with JavaScript and React

Master Neural Networks: Build with JavaScript and React, available at $84.99, with 101 lectures, and has 1 subscribers.

You will learn about Understand and implement perceptrons (single neuron) for binary classification Learn and apply neural network fundamentals in code Integrate neural networks into web applications using JavaScript and React Work with large-scale data, understanding and parsing it effectively This course is ideal for individuals who are Beginners who want a comprehensive, step-by-step guide to neural networks or Anyone interested in learning neural networks using JavaScript and React or Web developers looking to enhance their skills with AI It is particularly useful for Beginners who want a comprehensive, step-by-step guide to neural networks or Anyone interested in learning neural networks using JavaScript and React or Web developers looking to enhance their skills with AI.

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Summary

Title: Master Neural Networks: Build with JavaScript and React

Price: $84.99

Number of Lectures: 101

Number of Published Lectures: 101

Number of Curriculum Items: 101

Number of Published Curriculum Objects: 101

Original Price: $139.99

Quality Status: approved

Status: Live

What You Will Learn

Understand and implement perceptrons (single neuron) for binary classification

Learn and apply neural network fundamentals in code

Integrate neural networks into web applications using JavaScript and React

Work with large-scale data, understanding and parsing it effectively

Who Should Attend

Beginners who want a comprehensive, step-by-step guide to neural networks

Anyone interested in learning neural networks using JavaScript and React

Web developers looking to enhance their skills with AI

Target Audiences

Beginners who want a comprehensive, step-by-step guide to neural networks

Anyone interested in learning neural networks using JavaScript and React

Web developers looking to enhance their skills with AI

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

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

3. Learn how perceptrons work at a fundamental level.

4. Building a Simple Perceptron

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

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

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

8. Perceptron for Number Recognition

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

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

11. Learn techniques to calculate accuracy and handle misclassified data.

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

13. Parsing and Preprocessing MNIST Data

14. Learn to parse and preprocess MNIST data yourself.

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

16. Building a Multi-Layer Perceptron (MLP)

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

18. Implement training algorithms and understand backpropagation.

19. Explore various activation functions like ReLU and Softmax.

20. Practical Implementation with JavaScript and React

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

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

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

24. Integrate TensorFlow library

25. Learn to setup Neural networks with TensorFlow

26. Use Tensorflow 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: How to approach the lectures

Lecture 3: Few words before start

Chapter 2: Neuron vs Perceptron

Lecture 1: Initial Setup

Lecture 2: Neuron

Lecture 3: Biological neuron vs perceptron

Chapter 3: Classify objects

Lecture 1: Define data

Lecture 2: Define data in code

Lecture 3: Weighted sum

Lecture 4: Change the weight

Lecture 5: Update weights

Lecture 6: Compute sums in code

Lecture 7: Update weights for all inputs

Lecture 8: Update weights in code

Lecture 9: Measure accuracy

Lecture 10: Testing data

Lecture 11: Init weights randomly

Lecture 12: Measure acuracy each epoch

Chapter 4: Mnist Dataset

Lecture 1: Mnist data

Lecture 2: Read bytes

Lecture 3: Read info bytes

Lecture 4: Show label file

Lecture 5: Parse labels out

Lecture 6: Parse out images

Lecture 7: Save testing data

Chapter 5: Frontend in React

Lecture 1: Init react app

Lecture 2: Init home and navigation

Lecture 3: Basic router

Lecture 4: Finish routing

Lecture 5: Load mnist data

Lecture 6: Batch the data

Lecture 7: Display all labels

Lecture 8: Display images

Chapter 6: Real data training

Lecture 1: Save training data

Lecture 2: Process labels and inputs

Lecture 3: Train the perceptron

Lecture 4: Testing accuracy

Lecture 5: Show misclassified data

Lecture 6: Export model

Chapter 7: Prediction on Frontend

Lecture 1: Fetch model on frontend

Lecture 2: Make predictions

Lecture 3: Display prediction visualy

Lecture 4: New image prediction page

Lecture 5: Canvas preparation

Lecture 6: Draw on cavnas

Lecture 7: Get inputs from canvas

Lecture 8: Make prediction from canvas

Lecture 9: Clear canvas and display prediction

Chapter 8: Improving the model

Lecture 1: Adjust pixel values

Lecture 2: Experimenting with training

Lecture 3: Get misclassified data ready

Lecture 4: Send data to server

Lecture 5: Store misclassified data

Lecture 6: Simple perceptron wrap up

Chapter 9: Neural Networks – Forward Propagation

Lecture 1: Mlp introduction

Lecture 2: Mlp Finish Network

Lecture 3: Forward pass hidden activations

Lecture 4: Mlp data in code

Lecture 5: Compute hidden sum in code

Lecture 6: Compute hidden activations in code

Lecture 7: Hidden to output sums math + code

Lecture 8: Softmax explanation + math

Lecture 9: Additional info

Lecture 10: More explanation – recap

Lecture 11: Compute output probabilities

Chapter 10: Neural Networks – Backward Propagation

Lecture 1: Code cleanup

Lecture 2: Calculate output deltas

Lecture 3: Delta hidden neuron 1

Lecture 4: Delta Hidden neuron 2

Lecture 5: Hidden deltas in code

Lecture 6: Gradient of loss math

Lecture 7: Update hidden output weights math

Lecture 8: Update hidden output weights in code

Lecture 9: Weights input hidden math

Lecture 10: Weights input hidden code

Chapter 11: Neural Networks – Model Training

Lecture 1: More training data

Lecture 2: Init weghts randomly

Lecture 3: Loss function

Lecture 4: Measure accuracy of NN

Chapter 12: Neural Networks – Train MNIST Dataset

Lecture 1: Generate mlp data

Lecture 2: Load mlp data

Lecture 3: Encode labels

Lecture 4: Train the mlp model

Lecture 5: Improve logging

Lecture 6: Save mlp model

Lecture 7: Improving mlp model

Chapter 13: Neural Networks – Frontend

Lecture 1: Prepare mlp fronted page

Instructors

Eincode by Filip Jerga
Online Education

Filip Jerga
Software Engineer

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