This is in continuation of the last post where I scraped images from Google Search. In this part we will see the training of the model using Neural Networks with images as input. For training the model, I have utilised the Google Colab environment because training Neural Networks for image processing is memory intensive. Google Colab provides free GPU access for faster processing making it ideal for developers like me. The training data was uploaded on the drive manually.

In this blog we gonna discuss about the 10 most asked question from data science, I am assuming that you are a Fresher with 2 years of experience. With More experience questions could change from candidate to candidate. Before starting, I want to clarify one thing, "These set of questions are very common in my experience but can vary company to company". I have a request for you all, please read the questions, try to implement it on your own and then jump to the solutions. This struggles with the questions will make you good at programming, nothing else.

This article was published as a part of the Data Science Blogathon. In the first part of the series, we saw some most common techniques which we daily use while cleaning the data i.e. text cleaning in NLP. I would recommend if you haven't read it first read it, which will help you in text cleaning. The Link for the article is here. You can find the GitHub link here and start practicing and get your hand on the problem.

This blog is my first ever step towards applying deep learning techniques to Image data. This will be more of a practical blog wherein, I will be discussing how you can do a task like image classification without having much theoretical knowledge of mathematical concepts that lay the foundation of the deep learning models. I have been listening to all the amazing results (better than humans in some cases) that people have been producing for this task. I have been reading many blogs regarding VGG (Visual Geometry Group from Oxford) Model on how using this model people are making state-of-the-art models for image classification. In 2014, researchers from Oxford Visual Geometry Group(VGG) developed a CNN model for ILSVRC challenge.