UNIT 3- Introduction to DL(Deep Learning) in AI

UNIT 3- Introduction to DL(Deep Learning) in AI

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  VIDYABHARTI TRUST COLLEGEOF BUSINESS, COMPUTER-SCIENCE & RESEARCH Deep Learning
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  TABLE OF CONTENTS DL Overview  ML Vs DL  DL Application  Various DL algorithm
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  DEEP LEARNING (DL) Thedefinition of Deep learning is that it is the branch of machine learning that is based on artificial neural network architecture. An artificial neural network or ANN uses layers of interconnected nodes called neurons that work together to process and learn from the input data. Deep learning AI can be used for supervised, unsupervised as well as reinforcement machine
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  ML VS DL Machine LearningDeep Learning Apply statistical algorithms to learn the hidden patterns and relationships in the dataset. Uses artificial neural network architecture to learn the hidden patterns and relationships in the dataset. Can work on the smaller amount of dataset Requires the larger volume of dataset compared to machine learning Better for the low-label task. Better for complex task like image processing, natural language processing, etc. Takes less time to train the model. Takes more time to train the model. A model is created by relevant features which are manually extracted from images to detect an object in the image. Relevant features are automatically extracted from images. It is an end-to- end learning process. Less complex and easy to interpret the More complex, it works like the black
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  DL APPLICATION Deep learningcan be used in a wide variety of applications, including: Image recognition: To identify objects and features in images, such as people, animals, places, etc. Natural language processing: To help understand the meaning of text, such as in customer service chatbots and spam filters. Finance: To help analyze financial data and make predictions about market trends. Text to image: Convert text into images, such as in the Google Translate app.
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  DL ALGORITHM 1. Feedforwardneural networks (FNNs): Feedforward neural networks (FNNs) are the simplest type of ANN, with a linear flow of information through the network. FNNs have been widely used for tasks such as image classification, speech recognition, and natural language processing.
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  DL ALGORITHM 2. ConvolutionalNeural Networks (CNNs): Convolutional Neural Networks (CNNs) are specifically for image and video recognition tasks. CNNs are able to automatically learn features from the images, which makes them well-suited for tasks such as image classification, object detection, and image segmentation.
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  DL ALGORITHM 3. RecurrentNeural Networks (CNNs): Recurrent Neural Networks (RNNs) are a type of neural network that is able to process sequential data, such as time series and natural language. RNNs are able to maintain an internal state that captures information about the previous inputs, which makes them well-suited for tasks such as speech recognition, natural language processing, and language translation.