MODULAR MACHINE LEARNING

  Photo by krakenimages from unsplash One of the most incredible things about practicing Python is its continuity of open-source  libraries. There is a library for fundamentally anything. If you have studied some of my preceding blogs, you may have remarked that I’m a big supporter of low-code libraries. That’s not because I’m procrastinating to type code but because I fancy investing my time operating on a project with values. If a library can work a problem, why not preserve your valuable time and give it a try? Today, I will present you with five libraries you have never heard about, but you should attach them to your portfolio. Let’s get started! ----------------------------------------------------------------------------------------------------------------------------- PS: There are lots of amazing resources out there for learning ML and data science. My personal favorite is  DataCamp . This is where I started my journey and trust me it’s amazing and worth your time....

Have you ever wondered which optimization algorithm to use for your Neural network Model to produce slightly better and faster results by updating the Model parameters such as Weights and Bias values? Should we use Gradient Descent or Stochastic gradient Descent or Adam? I too didn’t know about the major differences between these different types of Optimization Strategies and which one is better over another before writing this article. NOTE:  Having a good theoretical knowledge is amazing but implementing them in code in a real-time deep learning project is a completely different thing. You might get different and unexpected results based on different problems and datasets.   So as a Bonus,I am also adding the links to the various courses which has helped me a lot in my journey to learn Data science and ML, experiment and compare different optimization strategies which led me to write this article on comparisons between different optimizers while implementing deep l...