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Machine Learning in Frontend Development

What is Machine Learning by Examples?

Making computers able to identify cats or dogs or anything in an image. Making computers able to predict the future.

Some of Algorithms that makes it possible


  1. Naive Bayes
  2. K-Nearest Neighbour (KNN)
  3. Linear Regression
  4. Convolutional Neural Networks (CNN)
  5. LSTM - Long Short Term Memory

Types of Algorithms


  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

What is Supervised Learning?

Create predictive models based on a set of features and labels. Example, predicting price of a house based on other houses sold in the area. Features are characteristics of the entries in the dataset. Label would be the prices of the houses. Another example is customer clustering in a supermarket based on buying habits, time of buying, frequency, etc. Another example is sign language to text or speech conversion.
One of the ways to use supervised learning in frontend development is setting alt text of images, adding tags to content, etc.

Why Machine Learning in Javascript?

Because most frontend develoeprs are good at javascript. One can reduce time spent on learning a new language such as python and directly focus on Machine Learning. That helps faster prototyping.
How can Machine Learning be used in frontend?
  1. Import an existing pre-trained model
  2. Retrain an imported model (transfer learning)
  3. Define, train, and run mdoels entirely in the browser.

Tools

Libraries
  1. ML5
  2. Tensorflow.js
  3. Keras.js
  4. etc...
Cloud ML Providers
  1. GCP
  2. AWS
  3. Microsoft
  4. etc...
Here is an example of a frontend ML that I developed using a pre-trained model: https://weekay.herokuapp.com/api/faice

How to train a model?

Take 80% of the dataset and label them. And let the machine learning program assess the rest of the 20% of datset and label them. For this the program needs a algorithm model.

Limitations

  1. Machine Learning programs can take large amount of space, time, computation power, energy.
  2. Results from machine learning programs cannot be explained, so if in cases where explanation is required, there it could be tricky.
  3. Results can be morally unacceptable
  4. Results can be biased because of the data input

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