To most people, deep learning and machine learning phrases sound like the interchangeable buzzwords of the AI universe. However, that isn’t true. Anyone who wishes to better understand the field of artificial intelligence should therefore begin by understanding the terms and their distinctions.
Deep learning is a specialized subset of machine learning, which in turn is a subset of artificial intelligence, to break it down into a single sentence. In other words, deep learning is machine learning.
If you are familiar with machine learning and the basic algorithms used in the field, then you may have heard of the algorithm k-nearest neighbors or KNN. One of the more simple techniques used in machine learning is this algorithm. Due to its ease of use and low calculation time, it is a process preferred by many in the industry.
And what’s KNN?
A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems is the k-nearest neighbors (KNN) algorithm.
In k-NN classification, the output is a class membership. An object is classified…
You’ll learn how to set up Xcode Simulator when working with Flutter in Android Studio. I found that many people are not aware of this so I penned down this article!
Machine Learning, what is it?
Machine learning is the method of creating models that can perform a certain task without the need for something to be explicitly programmed by a human.
In simple terms, Machine Learning is teaching your computer about something. It could be to distinguish between a dog and a cat, to diagnose patients with cancer, to create a chatbot that helps someone with depression.
The list of handpicked books that are useful in building core pillars for machine learning are as follows:-
Absolute Beginners’ Machine Learning is for anyone who is completely new to it. You may…
What is Transfer Learning?
Transfer learning is a technique that shortcuts much of this by taking a piece of a model that has already been trained on a related task and reusing it in a new model.
Here I am using google colab which gives ram and GPU integration in the browser :)
Step 1: Installation
Step 2: Setup Input Pipeline
Downloading the flower dataset…
Use ImageDataGenerator to rescale the images.
Create the trained generator and specify where the train dataset directory, image size, batch size.
Create the validation generator with a similar approach as the trained generator…
The objective of this article is to present the reader with a class in python that has a very intuitive and easy input to model and predict time series data using deep learning. Ideally, the reader should be able to copy the code presented in this article or the GitHub repository, tailor it to his needs (add more layers to the model for example) and use it in his/her work.
All the code that is used in this article can be found here:
The data for this article can be found here:
The packages that are used for deep modeling…