I have experience with convolution neural networks. Which works great for Image recognition. I am working on a similar project to recognize a handwritten mathematical equation and convert it into latex format.
If we can recognize a product by taking a picture we ca build a system such that we get details like brand name, price, nutrition facts, in which nearby stores product is available . Furthermore a recommendation system can be build to recommend other similar products based on what other similar users liked.
In Ingredient categorization ingredients can be classified based on classification algorithms like SVM's, KNN's, Naivebayes or several other classifiers. I am proficient applying them to data.
Many people follow diet plans they have restriction for the amount of calorie intake. Very specifically they calculate amount of protein, carbohydrates and fats in the food. By having nutrition facts of the products, items can be picked such that they don't cross the dietary limit.
For a same product different brands have different prices. In another case When I have X amount of budget and I want to purchase items A, B, C. we can recommend products such that customers can pick brands so that the budget is not crossed.
When a customer is in a grocery store. when entered a product name we can give the aisle the product is in. (speech recognition). same functionalities which can be added to image recognition can be added to speech. I have good knowledge on neural networks and hands on experience with tensor-flow a deep learning library. I have done many academic projects with tensor-flow.
With my skills I can also help in data collection, integration, data cleaning, data visualization.
I have experience with convolution neural networks. Which works great for Image recognition. I am working on a similar project to recognize a handwritten mathematical equation and convert it into latex format.
If we can recognize a product by taking a picture we ca build a system such that we get details like brand name, price, nutrition facts, in which nearby stores product is available . Furthermore a recommendation system can be build to recommend other similar products based on what other similar users liked.
In Ingredient categorization ingredients can be classified based on classification algorithms like SVM's, KNN's, Naivebayes or several other classifiers. I am proficient applying them to data. Many people follow diet plans they have restriction for the amount of calorie intake. Very specifically they calculate amount of protein, carbohydrates and fats in the food. By having nutrition facts of the products, items can be picked such that they don't cross the dietary limit.
For a same product different brands have different prices. In another case When I have X amount of budget and I want to purchase items A, B, C. we can recommend products such that customers can pick brands so that the budget is not crossed.
When a customer is in a grocery store. when entered a product name we can give the aisle the product is in. (speech recognition). same functionalities which can be added to image recognition can be added to speech. I have good knowledge on neural networks and hands on experience with tensor-flow a deep learning library. I have done many academic projects with tensor-flow.
With my skills I can also help in data collection, integration, data cleaning, data visualization.