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If you need to deploy a machine learning model to a mobile device, it becomes challenging, as there’s limited space and processing power on the device. There’s no doubt that machine learning models suffer from such heavy model sizes and high latency when targeting mobile devices.

However, there are techniques to reduce size or increase performance so that they do fit and work on mobile (see the links below for more on these techniques). It should be noted that, despite these challenges, ML models are currently being deployed to mobile devices.

In this article, we’re going to discuss how to implement a housing price prediction machine learning model for mobile using TensorFlow Lite. We’ll learn how to train a TensorFlow Lite neural network for regression that provides a continuous value prediction, specifically in the context of housing prices.

TensorFlow Lite is an open source deep learning framework for mobile device inference. It is essentially a set of tools to help us run TensorFlow models on mobile, embedded, and IoT devices. TensorFlow Lite enables on-device machine learning inference with low latency and a small binary size.

There are two main components of TensorFlow Lite:

- TensorFlow Lite interpreter: The interpreter runs optimized Lite models on many different hardware types, including mobile phones, embedded devices, and microcontrollers.
- TensorFlow Lite converter : The converter basically converts TensorFlow models into an efficient form to be used by the interpreter. This can introduce optimizations to improve binary size as well as performance.

#machine-learning #tensorflow #tensorflow-lite #heartbeat #mobile-app-development

1601323200

If you need to deploy a machine learning model to a mobile device, it becomes challenging, as there’s limited space and processing power on the device. There’s no doubt that machine learning models suffer from such heavy model sizes and high latency when targeting mobile devices.

However, there are techniques to reduce size or increase performance so that they do fit and work on mobile (see the links below for more on these techniques). It should be noted that, despite these challenges, ML models are currently being deployed to mobile devices.

In this article, we’re going to discuss how to implement a housing price prediction machine learning model for mobile using TensorFlow Lite. We’ll learn how to train a TensorFlow Lite neural network for regression that provides a continuous value prediction, specifically in the context of housing prices.

TensorFlow Lite is an open source deep learning framework for mobile device inference. It is essentially a set of tools to help us run TensorFlow models on mobile, embedded, and IoT devices. TensorFlow Lite enables on-device machine learning inference with low latency and a small binary size.

There are two main components of TensorFlow Lite:

- TensorFlow Lite interpreter: The interpreter runs optimized Lite models on many different hardware types, including mobile phones, embedded devices, and microcontrollers.
- TensorFlow Lite converter : The converter basically converts TensorFlow models into an efficient form to be used by the interpreter. This can introduce optimizations to improve binary size as well as performance.

#machine-learning #tensorflow #tensorflow-lite #heartbeat #mobile-app-development

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Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

**Classification Model:**It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.**Clustering Model:**This model groups data points into separate groups, based on similar behavior.- **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
**Outliers Model:**This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.**Time Series Model:**This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models

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In this final part, I will share some popular machine learning algorithms to predict the housing prices and the live model that I have built. My objective is to find a model that can generate a high accuracy of the housing prices, based on the available dataset, such that given a new property and with the required information, we will know whether the property is over or under-valued.

I explore 5 machine learning algorithms that are used to predict the housing prices in Singapore, namely multi-linear regression, lasso, ridge, decision tree and neural network.

Multi-linear regression model, as its name suggest, is a widely used model that assumes linearity between the independent variables and dependent variable (price). This will be my baseline model for comparison.

Lasso and ridge are models to reduce model complexity and overfitting when there are too many parameters. For example, the lasso model will effectively shrink some of the variables, such that it only takes into account some of the important factors. While there are only 17 variables, in the dataset and the number of variables may not be considered extensive, it will still be a good exercise to analyse the effectiveness of these models.

Decision tree is an easily understandable model which uses a set of binary rules to achieve the target value. This is extremely useful for decision making as a tree diagram can be plotted to aid in understanding the importance of each variable (the higher the variable in the tree, the more important the variable).

Last, I have also explored a simple multi-layer perceptron neural network model. Simply put, the data inputs is put through a few layers of “filters” (feed forward hidden layers) and the model learns how to minimise the loss function by changing the values in the “filters” matrices.

#predictive-analytics #predictive-modeling #machine-learning #sklearn #housing-prices

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TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform the specific tasks of embedded systems.

In a workshop held as part of Google I/O, TensorFlow founding member Pete Warden delved deep into the potential use cases of TensorFlow Lite for microcontrollers.

Further, quoting the definition of TinyML from a blog, he said:

“Tiny machine learning is capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of ways-on-use-case and targeting battery operated devices.”

#opinions #how to design tinyml #learn tinyml #machine learning models low cost #machine learning models low power #microcontrollers #tensoflow latest #tensorflow lite microcontrollers #tensorflow tinyml #tinyml applications #tinyml models

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📺 The video in this post was made by K Crypto

The origin of the article: https://www.youtube.com/watch?v=-uWpSe8GuP0

🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.

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