DEEP LEARNING model of rnn cnn_Rushi.pptx

Deep learning

1. TYPES OF DEEP LEARNING Artificial neural networks  (ANN)   Convolutional Neural Network (CNN)   Recurrent neural network (RNN) DEEP LEARNING RUSHIKESH KHEKADE 25/02/2023

2. DEEP LEARNING What is deep learning? Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans

3. Deep learning eliminates some of data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing some of the dependency on human experts. Why deep learning is used?

4. Where is deep learning used ? Deep learning is currently used in most common image recognition tools, natural language processing (NLP) and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services. Fraud detection. Customer relationship management systems. Computer vision. Vocal AI. Natural language processing. Data refining. Autonomous vehicles. Supercomputers. Common Deep Learning Applications

5. Artificial neural networks  (ANN) Artificial neural network (ANN) is a computational model that consists of several processing elements that receive inputs and deliver outputs based on their predefined activation functions What is Artificial neural networks (ANN)?

6. Why Artificial neural networks (ANN) is used? Artificial neural networks are created to digitally mimic the human brain. They are currently used for complex analyses in various fields, ranging from medicine to engineering, and these networks can be used to design the next generation of computers 

7. Advantages of Artificial Neural Network black box nature greater computational burden proneness to over fitting empirical nature of model development A neural network can implement tasks that a linear program cannot. When an item of the neural network declines, it can continue without some issues by its parallel features. A neural network determines and does not require to be reprogrammed. It can be executed in any application . Disadvantages of artificial neural networks

8. Applications of Artificial Neural Network Social Media   Marketing and Sales   Healthcare    Personal Assistants  

9. Convolutional Neural Network(CNN) What is Convolutional Neural Network(CNN) A convolutional neural network ( CNN) is a network architecture for deep learning that learns directly from data. CNNs are particularly useful for finding patterns in images to recognize objects, classes, and categories

10. Advantages of Convolutional Neural Network(CNN) No human supervision High accuracy at image recognition Weight sharing Minimize computation Same knowledge across all image location Disadvantages of Convolutional Neural Network(CNN) A lot of training data is needed CNNs tend to be much slower Training process takes a long time Fail to the encode position & orientation of objects

11. Applications of Convolutional Neural Network(CNN) Facial recognition Medical imaging Document analysis Autonomous driving

12. Recurrent neural network(RNN) What is Recurrent neural network(RNN) Recurrent neural networks (RNNs) are the state of the art algorithm for sequential data. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data.

13. Advantages of Recurrent neural network(RNN) RNN can model a collection of records (i.e. time collection) so that each pattern can be assumed to be dependent on previous ones. Recurrent neural networks are even used with convolutional layers to extend the powerful pixel neighbourhood. Gradient exploding and vanishing problems. Training an RNN is a completely tough task. It cannot system very lengthy sequences if the usage of Tanh or Relu as an activation feature. Disadvantages of Recurrent neural network(RNN)

14. Applications of Recurrent neural network(RNN) Machine Translation Speech Recognition Video Tagging Text Summarization

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