Learn Data Science using Python

Another post starts with you beautiful people! It was quite overwhelming to see almost 10,000 views on my last post where we learned to build a simple  Keras + Deep Learning Rest API . That post is quite important if you want to deploy your model as a Rest API in development environment so that you can easily demonstrate it to your tester team or business team. One drawback with this approach is that it was intended for single threaded use only with no concurrent requests but in production environment your model will be used by many users at the same time so it is important to efficiently batch process incoming inference requests. Many thanks to  Adrian   who has shared his knowledge to use  Redis   to solve this problem and make our simple keras Rest Api scalable. If you are macos/linux user then you can follow  Adrian's post   but if you are a Windows user like me then you must follow this post because officially Redis does not support Wind...

Another post starts with you beautiful people! In my last  post we have built a digit recognition deep learning model using Keras and achieved 97% of accuracy. If you have just started your deep learning journey; I recommend you to visit my last three posts before starting this post. Keras provides various deep learning models like VGG16, ResNet50, NASNet etc with pre-trained weights which can be used as a base model for your work. You can easily fine tune them as per your requirement without writing a code from scratch. You can find all the details of these model in this link  . These models' weights are pre-trained on the ImageNet dataset . So you can use these models in your image classification task and they can recognize more than 1,000 common object classes out-of-the-box. In this post I am going to share how to use one of these models to identify an object from the given image and then deploy this model as a Rest API using  flask framework . You will also l...