Hacker's Guide to Neural Networks in JavaScript
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Hacker's Guide to Neural Networks in JavaScript

Beginners guide to understanding Machine Learning in the browser with TensorFlow.js

About the Book

A hands-on approach to understanding the basics of Machine Learning, build models and deploy real-world solutions using TensorFlow.js with JavaScript. Run your models in the browser or on the server with Node.js.

Solve real-world problems and learn about Computer Vision, Natural Language Processing, Deep Reinforcement Learning by building and training Deep Neural Networks in JavaScript.

A blend of theory, simple explanations, examples, and complete source code serve as a thorough introduction to the beginner, while the experienced professional will enjoy the practical solutions to common problems.

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    • Computers and Programming
    • Sciences
    • Business Analysis
    • JavaScript
    • Computer Science
    • Data Science
    • Machine Learning
    • Artificial Intelligence
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    Contact the Author(s)

About the Author

Venelin Valkov
Venelin Valkov

Hello there!

I'm excited to welcome you to the wonderful world of Machine Learning! My name is Venelin and I'm thrilled to be your guide on this journey.

With over 5 years of experience in Machine Learning and 10+ years in Software Development, I have worked on several exciting projects, ranging from sentiment analysis, price prediction, and document text extraction to training Object Detection algorithms. Currently, my focus is on Large Language Models such as ChatGPT, which has been a fascinating area of exploration for me.

I look forward to sharing my knowledge and experience with you and helping you discover the limitless possibilities that Machine Learning offers. Let's embark on this adventure together!

Table of Contents

  • Making Predictions with a Simple Neural Network from Scratch
    • Simple Neural Network for predictions
    • Predicting with multiple inputs
    • Predicting multiple outputs
    • TensorFlow.js
    • Summary
    • References
  • Training a Neural Network from Scratch with Gradient Descent
    • Learning is error reduction
    • Learning by going up and down
    • Learning with Gradient Descent
    • Summary
    • References
  • Training a Deep Neural Network with Backpropagation
    • Generalized Gradient Descent
    • Backpropagation
    • Training a Deep Neural Network with Backpropagation
    • Summary
    • References
  • Build a simple Neural Network with TensorFlow.js
    • The magic of Tensors
    • Getting derivatives of a function
    • Training a Deep Neural Net
    • Making predictions
    • Summary
    • References
  • Getting Started with TensorFlow.js
    • Tensors
    • Visualization with tfjs-vis
    • Train your first model
    • Conclusion
  • Predicting Diabetes using Logistic Regression
    • Diabetes data
    • Logistic Regression
    • Predicting diabetes
    • Conclusion
    • References
  • House Price Prediction using Linear Regression
    • House prices data
    • Linear Regression
    • Data Preprocessing
    • Predicting house prices
    • Conclusion
    • References
  • Build a simple Neural Network
    • Neural Networks
    • Should you buy the laptop?
    • Conclusion
    • References
  • Customer churn prediction using Neural Networks
    • Customer churn data
    • Deep Learning
    • Predicting customer churn
    • Conclusion
    • References
  • Alien vs Predator image classification using Deep Convolutional Neural Networks
    • Data
    • Convolutional Neural Networks
    • Classifying Aliens and Predators
    • Building a Deep Convolutional Neural Network
    • Conclusion
    • References
  • ToDo List text classification using Embeddings and Deep Neural Networks
    • ToDo app in ReactJS
    • Data
    • Embeddings
    • Suggesting icons for ToDos
    • Deployment
    • Conclusion
    • References
  • Burglar alarm system using Object Detection
    • Using pre-trained models
    • Object Detection
    • Finding intruders
    • Conclusion
    • References

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