Learn Data Science using Python

Another post starts with you beautiful people! I hope you have enjoyed my  last post   about  image data augmentation  and now you are comfortable with increasing the size of your small training dataset. Today's post is going to be interesting because we are going to learn how to handle color images and examples of using convolution with keras. Let's starts our learning- In our first post of this series we have seen that typically an image can be stored in 3-dimensional format- one is for height, one is for width and one is for channels. Colored images have three channels- red,blue,green components. A deep learning network requires that image data should be provided as a 3-D arrays. There are 2 ways to represent the image data as a 3-D array. First way is known as ' channel last ' and second way is known as ' channel first '. In 'channel last', last channel represents the color channels while in 'channel first', first channel represents t

Another post starts with you beautiful people! I hope you have enjoyed my  last post   and now you have become familiar with image loading, manipulation and saving in deep learning. If you have not read my last post then I recommend you to read the  first part   of this series. In this second part of my post we will learn about one of the most important technique used for a small image training dataset- Image   Data Augmentation ! 1. What is Image Data Augmentation? Image Data Augmentation is a technique to expand the size of a training dataset. This technique can create modified versions of images which helps when we have a small dataset. Image shifting, flipping and zooming are some examples of this technique. Keras provides ImageDataGenerator class for this purpose. 2. How to use this technique? We will learn different uses of this technique one by one by applying on an image- A. How to shift an image? Shifting an image means we are moving all pixels in one direction

Another post starts with you beautiful people! Today we are going to enter into the Computer Vision (CV) field with this first post. With this and further coming posts, we will learn how to use Deep Learning for CV. We will cover following three key concepts with Python code in this post- 1. What is CV? 2. How to load and manipulate image using Keras? 3. How to scale your image using Keras? Let's starts our learning- 1. What is CV?  Computer Vision or CV is a sub-field of Machine Learning and Artificial Intelligence, focused on the problem of helping computers to see. The goal of CV is to understand the content of digital images. CV has a wide variety of tasks- Object Classification Object Identification Object Verification Object Detection Object Segmentation Object Recognition 2. How to load and manipulate image using Keras? A. Loading an image using Keras api is very easy. Keras provides load_img() function for loading an image. Before moving furt