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Deep Learning Primer
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
In order to simulate Biological Neural Network, artificial neural networks are utilised.
Because there are several layers leading to the output, deep learning is given this term.
Shallow Learning Models vs. Deep Learning Models
Deep Learning models are used to automatically extract features from the data (images, speech, text, and videos; movies are a subset of images).Click here to learn Data Science in Hyderabad
The multiple layers capture compositionality:
Image Recognition
Each layer captures certain characteristics, such as:
Low-Level characteristics are captured by the first layers.
The next layers record mid-level characteristics.
High-Level characteristics are captured by final layers.
The classifier will eventually forecast the result.
Deep Learning Learns Layers of Features
Layers of Features are Learned by Deep Learning
Multiple layers of processing are applied to speech data in order to capture compositionality.
Deep Learning layers are used to analyse text input and identify compositionality.
Shallow Machine Learning Models:
Manual feature extraction from the data is done.
Vision/Images/Videos: Videos are divided into frames of images, and the features are then retrieved from each frame of image.
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