Think Logically
How much time we have spent in order to learn ‘ABCD..’ it’s about a year. Now, we are living in the era of machines and assume your machine asks you to teach ‘Hindi’ –the fourth most speaking language around the world. With the help of deep learning, it is too easy and effective. So moving …
When we start communicating with a machine there is only one issue machine never understand different categories by name. If we tell a machine the colour of a balloon is red it will not understand ‘Red’ rather than it will keep it as 255,0,0 0r 1,0,0 it means it encodes it in its own mother …
When we enter into the world of computer vision we have to understand how a computer understands an image. A colored image has three channels and a 2D data in each channel. When the image size increases Machine learning start suffering from the curse of dimensionality, in order to overcome from this Deep learning comes up …
Many times we need to visualize our model on Tensorboard, for this we have to save our model and at runtime check out the performance. Here is the code for a simple linear regression using Keras and tensorboard. import Libraries: import keras import numpy as np from pandas import read_csv from keras.models import Sequential from …
Machine Learning algorithms unsatisfied problem with classifiers when faced with imbalanced datasets. Let’s take an example of the Red-wine problem. The Data we have is as: Here we have a data set in which we have different six categories, but not balanced categories. So, in the case available the samples are not equally balanced so …
Introduction Supervised machine learning has two types: 1) Regression, 2) Classification In regression, we deal with the continuous data type that means the predicted value or target value will be the type of continuous in nature but in classification problems, we basically deal with target value as discrete in nature it belongs to some class. Let’s understand some use …