According to the last few years records, neural networks (NNs) have really taken off as a practical and efficient way of solving problems like face detection, voice recognition, and medical diagnosis. StackOverflow posts about how JS is a terrible language for Machine Learning. JavaScript developers tend to look out for JavaScript frameworks that can be used to train machine learning models based on different machine learning algorithms. The highly technical nature of NNs and all of the jargon that goes along with it makes it hard for the uninitiated to take advantage. This is where Brain.js comes in to play.
Brain.js is a set of JavaScript libraries for training neural networks and Naive-Bayesian classifier. It does a great job simplifying the process of creating and training an NN by utilizing the ease-of-use of JavaScript and by limiting the API to just a few method calls and options.
While Brain.js doesn't have a ton of options to allow you to customize your networks, the API accepts enough parameters to make it useful for simple applications. You can set the number and size of your hidden layers, error threshold, learning rate, and more.
Brain.js is great for quickly creating a simple NN in a high-level language where you can take advantage of the huge number of open source libraries. With a good dataset and a few lines of code, you can create some really interesting functionality.
Today's most popular mobile apps use machine learning to their benefit, it’s time to figure out the way to use ML to your benefit. Here are some machine learning implementation ideas.
E-commerce apps
The use of machine learning in e-commerce mobile apps can provide relevant information to users while they search for products. With its help, the app can recommend them the right products based on their interests, and even analyze the fashion trends and sales information and give predictions in real-time.
Finance apps
Finance mobile apps with machine learning implementation can analyze the history of previous transactions and utilize the historical data to offer users unique deals that are going to be perfect for each specific user. Also, it can be used to manage users’ income and expenditures by linking their accounts and credit cards.
Sports forecasting apps
For the sports forecasting mobile apps, machine learning can be of great help. Machine learning model written right can predict the outcome of any sports game with extreme accuracy.
Healthcare apps
In the healthcare apps niche, machine learning can play the role of doctor/adviser. So it could analyze the symptoms and give the needed solutions. These ML apps also can forecast the possibility of a headache and recommend ways to prevent one.
Time management apps
Time management apps can employ machine learning to find suitable times for you to complete tasks and to prioritize things on your to-do list.
Weather forecasting apps
A weather app with ML can predict and send daily forecasts and alerts.
Transportations
Machine learning can be used to provide an estimated time of arrival and cost to riders, offer detailed real-time information on maps to drivers, and more.
Travel apps
A travel app that uses ML can allow users to ask questions about their future travels. So, this way users are provided with personal travel assistant.
Food or restaurant apps
The app with machine learning can take orders, answer and ask questions, suggest a perfect recipe. Based on your previous orders, the app can suggest a new item on the menu or deal users might like. For the food delivery apps, machine learning can offer delivery time estimate based on real-time traffic conditions.
Sports apps
Machine learning in a sports mobile app can read the sensors a and genetic data available to tailor a deeply individual workout program. It can also be used for tracking users training efforts and maintaining a personalized training journal for them.
Image editing apps
An image editing app that uses machine learning can offer a sizable selection of different photo filters that you can apply by just telling the bot what to do.
