Artificial intelligence, as the name suggests, makes a machine artificially intelligent by making the machine think or act like humans. It is advancing at a great pace and deep learning is one of the major contributors to that.
It is a sub-field of machine learning that deals with algorithms that are inspired by the structure and function of the brain called artificial neural networks. These are similar to how the Central Nervous system is structured where each neuron is connected to each other.
Imagine you would like to train a deep learning model where you have thousands of images, but your system does not have any GPU. It would be hard to train large training models without GPU, so you will generally use google collab to train your model using google’s GPU’s.
Consider your system memory is full, and you have important documents and videos to be stored and should be secured. Google drive can be one solution to store all your files, including documents, images, and videos up to 15GB, and offers security and back-up.
Above mentioned scenarios are some of the…
A comprehensive guide on how to perform polynomial regression
Artificial Intelligence (AI) and machine learning technology have been developing rapidly in recent years. We see both of them in our life daily. If you look behind the scenes, you can observe a lot of applications in existence ranging from medical applications to customer recommendations. The fields of AI are making a major breakthrough that no one has ever imagined.
If you are a newbie and need to brush up about regression, have a look at my articles on Linear Regression and Multiple Regression which helps you understand it.
All you need to know about Machine Learning
Artificial Intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks. People across different domains are trying to apply AI to make their jobs a lot easier.
These fields are developing rapidly in recent years. We see both of them in our life daily. We can observe many applications, ranging from medical applications to customer recommendations. The fields of AI are making a significant breakthrough that no one has ever imagined.
For example, doctors use AI applications to provide personalized medicine and X-ray…
Making data understandable
Considering the fact that high-quality data leads to better predictions, data preprocessing has become a fundamental step in data science and machine learning. We’ll talk about the importance of processing data and discuss different approaches in sequence.
It is a technique that transforms raw data into an understandable format. Real-world data(raw data) is always incomplete and that data cannot be sent through models as it would cause certain errors. That is why we need to preprocess data before sending it through a model.
Here are the steps I have followed;
We as humans have the ability to transfer the knowledge gained in one task to use it another task, the easier is the task the easier is to utilize the knowledge. Some simple examples would be:
Most of the Machine Learning and deep learning algorithms so far are designed to focus on solving specific tasks. …
If you are not aware of how Convolutional Neural Networks work, check out my blog below which explain about the layers and its purpose in CNN
We are going to work on cats and dogs dataset which means our network will predict whether the provided image is a cat or a dog. You can find the link for the dataset and code below
I took around 10,000 images from the dataset for easy computation and made a validation folder and placed some random images of cats and dogs to test the classifier after training.
The dataset contains two folders Train…
If you are new to Artificial Neural networks, you can check out my blog on Introduction to ANN in the below link
In this tutorial, we will build an Artificial Neural Network on Fashion MNIST dataset which consists of 70,000 images out of which 60,000 images belong to the training set and 10,000 images belong to the test set. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total with ten labels associated with them. The pixel-value is an integer between 0 and 255. Each row is a separate image…
The phenomenon that makes machines such as computers or mobile phones see the surroundings is known as Computer Vision. It is a field of Computer Science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do.
Some applications of Computer Vision includes:
Face Detection is not perfect but it has reached stages where it is accepted in our daily lives from unlocking the phone to sending money.
Convolutional Neural Networks are a part of Deep Learning which is employed in image recognition, image classification, object detection, etc. These take an image as input, processes it, and classify it under certain categories.
Here every image is passed through a series of filters, pooling, flattening, and a fully connected layer and apply as the softmax activation function to classify an object with probabilistic values between 0 and 1.
The main reason for applying softmax function is that, as an example, if we are classifying a cat and dog, and let’s say we got a probability that the image is…