Learn Data Science using Python

Another post starts with you beautiful people! In the past few months I was working on a complex object detection and recognition problem. My client is from a leading winery industry and they had an existing system built on  vgg19   and  keras-retinanet . This system was built to help client in their sales forecasting. The problem with current system was it was inaccurate- it did not detect most of the wine bottles and brands, it did not give the result in real time. You can imagine how this bad model can affect the business! To solve the existing issues I tried a lot of things- changing hyperparameters, increasing the datasets, different  keras applications  but it was not giving me satisfactory result. May be I was not doing it right but I had put a lot of time and efforts with it. Then while doing R&D, I read  this fantastic blog  and I came to know about a state of the art and real time object detection system-  YOLO . You Only Look...

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! From this post I am going to share my learning from the data camp  and various sources about the Deep Learning- a subfield of machine learning inspired by the structure and function of the brain (called artificial neural networks). Before jumping into the Python code, we must understand nuts and bolts of Deep Learning. That is what we are going to learn in this post. When you hear the term Deep Learning, just think of a large deep neural network. This network is so much powerful is that Deep Learning gives amazing results for text, images, audio and video data. For every problem, deep learning models capture interactions and how they capture the interactions we need to understand following three components- Input Layer :- situated in far left side in neural network and represents our predictive features. Output Layer :- situated in far right side and represents the prediction from our model Hidden Layer :- all layers t...