Introduction to Deep Learning

Hello guys, today we will learn what is deep learning and difference between Machine Learning and Deep Learning and also a small introduction about Neural Networks.

What is Deep Learning?

Deep learning is a type of machine learning (ML) and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling.

Difference between ML and DL

In machine learning, first we should extract features from input and then fed to the model. But when comes to Deep learning there will be no need of feature extraction. There is one of the most important algorithms called Neural Networks which we will discuss in this post.

Lets discuss some more concept about the difference between ML and DL. Before that lets know about different types of data. There are two types of data

Structured Data - Price of house, user ID etc.
Unstructured Data - Images, audio, text, etc.

Now, for ML models we can directly give input if it is a structured data. But when comes to unstructured data first we have to extract the features(pixel values for images, audio frequency for audio, etc.) and then fed to the ML model. Now, Deep Learning comes in the picture. Artificial Neural Networks itself extracts the features and fed as input to the model.

The above figure also show one of the main differences between ML and DL.

Artificial Neural Network

Artificial neural networks are popular machine learning techniques that simulate the mechanism of learning in biological organisms. The human nervous system contains cells, which are referred to as neurons. The neurons are connected to one another with the use of axons and dendrites, and the connecting regions between axons and dendrites are referred to as synapses.

There are 3 different types of ANN(Artificial Neural Networks)

Deep/Fully Connected Neural Network
Convolutional Neural Network
Recurrent Neural Network

That's all for this post. From the next post I'm going to start projects on Deep Learning. You can see my Introduction to Machine Learning post here

Thank you!

Contacts:

ph.No: +91 9182530027

gmail: hunnurjirao2000@gmail.com

github: github.com/hunnurjirao

Comments

Introduction to Machine Learning

Hello all, welcome to the practical machine learning hub. Here you will be learning some of the projects on machine learning and deep learning. But in this post let us know what exactly is machine learning and types of machine learning.

Generating Fake Faces

Hello guys, welcome to this new project of Generating Fake Faces. There is a wonderful concept called GANs (Generative Adversarial Network) which generates fake images. You can generate any thing you want. If you have person's faces dataset, you can generate fake faces of persons. If you have Pokémon's dataset, you can generate new Pokémon that never exists on the Earth. GANs were invented by Ian Goodfellow in 2014 and first described in paper Generative Adversarial Nets . If you have zero knowledge about GANs don't worry about it, you will learn how it works here and by the end of the post you will be able to generate whatever you want (only if you have a good and huge dataset!!!). One last thing I want to inform you is that this project is done using PyTorch . You can find the full code here . You can find more fake persons that doesn't exist on the earth here , once go through it. OK guys lets start our project. Generative Adversarial Networks Generative A...

Hand Signs Using KNN

Hello guys, welcome to this new project named "Hand Signs using KNN". There are different types of Hand Signs, but here we use only hand signs of Zero, One, Two, Three, Four & Five. But wait.! we are not using any Deep Learning or Neural Networks in this project. Instead we are using KNN a.k.a K-Nearest Neighbor which is one the Machine Learning Algorithms. We will talk about this later in this post. But how can we give directly images as input to the model, if we use a Machine Learning algorithm? Don't worry about it there are two files (train.h5, test.h5) that consists of features of the images (here features is nothing but pixel values of the image). You can find these files and also full code here . Hand Signs Now lets talk about K Nearest Neighbor. K Nearest Neighbor(A Supervised Learning Algorithm) Introduction KNN algorithm is the laziest algorithm , we can also say KNN as instance based learning. It is called as the laziest algorithm because it does nothin...

A Convolution Neural Network Application

Hello guys, welcome to this new project. In this project you will learn about Convolutional Neural Networks and the project is to Detect You whenever you are in-front of your laptop. One thing I want to conform before starting our project, this project is totally different from face detection. I will discuss about face detection in another session. And guys don't worry about code. 'Why worry, when we have Google's Tensorflow!'.You can find the code here , have a look. OK guys lets start our project. Convolution Neural Network Convolution Neural Network(CNN) Why CNNs? Let us start the session from this question - Why CNNs? We have DNN(Deep Neural Networks) which performs well on images but why CNNs? To know the reason, let us assume we have an RGB image of shape (64,64,3). This has 12288 (64*64*3) number of pixels (It is also called as 12288 Dimensional feature vector).It is not too bad! But as technology increased we can find images of (1000,1000,3) that has nearly 3 M...

MNIST Handwritten Digits Recognition

Hello guys, in this post we will learn about MNITS(Modified National Institute of Standards and Technology) Hand written digits recognition. Here we will be creating a Deep Neural Network model that recognizes the hand written digits. MNIST is nothing but a dataset that contains 60000 training images and 10000 testing images of hand written digits and these are actually gray scale images. You can find full code here The below are the sample images of MNIST dataset. Now lets dive into code!!! Setup First we will import some necessary modules like numpy and keras. Numpy is used to perform algebric or numerical operations and Keras is a deep learning API written in Python, running on top of the machine learning platform Tensorflow(Tensorflow is an end-to-end, open-source machine learning platform.) import numpy as np from tensorflow import keras from tensorflow.keras import layers Load the dataset Now lets load the MNIST dataset. (x_train, y_train), (x_test, y_test) = keras.da...

Deep Learning Projects Hub

Search This Blog