Convolutional neural network

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Convolutional neural network

What is a convolutional neural network?

Convolutional Neural Networks are deep learning models designed specifically for processing & analyzing visual data such as images & videos.

It is inspired by the structure of the visual cortex in the brain - that consists the cells sensitive to small regions of the visual field.

“Looking at a function’s surroundings to make better/accurate predictions of its outcome.”
- Dr. Prasad Samarakoon
convolutional neural network
Source: Towards Data Science

How do convolutional neural networks work?

1. CNNs work by processing input data through a series of layers where every layer performs a specific operation on the data.

2. The basic building block is a convolution layer - that applies a set of filters/kernels to the input data.

3. The feature maps are then passed through a non-linear activation function.

4. The output of the convolution is then downsized.

5. This process is repeated a couple of times to create the hierarchy of features.

6. Then the output is flattened & passed through one or more fully connected layers - that perform either classification or regression task.

What are the applications of convolutional neural networks?

uses of convolutional neural network

Here are some of the common applications of convolutional neural networks:

1. Semantic segmentation: CNNs can classify every pixel in an image into different classes, for e.g. - different types of vegetation in satellite images.

2. Object detection: CNNs can detect objects within an image, for e.g. - identifying the location & the type of vehicle on the road.

3. Image classification: CNNs can classify images into different categories, for e.g. identifying objects in a photograph.

4. Image captioning: CNNs can generate natural language descriptions of images, for e.g. - describing the objects in a photograph.

5. Face recognition - CNNs can recognize & verify the identity of different individuals in images, such as finding people's faces in secrurity footages.

6. Medical image analysis -CNNs can identify tumors in medical scans, or in detecting abnormaities in X rays.

7. Video analysis - CNNs can detect the movement of objects across frames.

8. Autonomous vehicles - CNNs can identify & track objects - such as pedestrians & other vehicles.

What are the advantages of convolutional neural networks?

Advantages of convolutional neural network

Here are the most significant advantages of convolutional neural networks (CNNs):

1. No require human supervision required

2. Automatic feature extraction

3. Highly accurate at image recognition & classification

4. Weight sharing

5. Minimizes computation

6. Uses same knowledge across all image locations.

7. Ability to handle large datasets

8. Hierarchical learning

What are the disadvantages of convolutional neural networks?

disadvantages of convolutional neural networks

1. High computational requirements

2. Needs large amount of labeled data

3. Large memory footprint

4. Interpretability challenges

5. Limited effectiveness for sequential data

6. Tend to be much slower

7. Training takes a long time

What is the difference between a convolutional and deep neural network?

The biggest difference between convolutional neural networks and other deep neural networks is that because hierarchical patch-based convolution operations are employed in CNNs, the computational costs are reduced and images are abstracted on different feature levels.

A CNN could be useful for reducing the number of parameters we need to train without you having to sacrifice on performance. However, training a conversational neural network tends to be a bit slower than training a DNN.

A DNN (Deep neural network) could be a convolutional neural network or it could be a plain multilayer perceptron. 

Is convolutional neural network better than recurrent neural network?

Convolutional neural networks are generally used to solve problems that are related to spatial data like images. RNNs are used more often to analyze temporal, sequential data like text or videos.

Their architectures are also different. CNNs are feed-forward neural networks which use filters and pooling layers, while RNNs feed the results back into the network. 

CNNS are considered to be rather powerful in comparison with RNNs, and RNNs use less feature compatibility compared with CNNs.

Using a convolutional neural network is best for processing images and videos, while recurrent neural networks analyzing text and speech. 

In CNNs, inputs should be of fixed sizes, and they’ll even generate outputs of fixed sizes. RNNs, on the other hand, can deal with inputs and outputs of arbitary lengths.

Architecture of Convolutional Neural Networks

Here are the main components:

1. Convolutional layers - apply a set of learnable filters to the input data, through the convolution operation.

2. Activation functions - introduce non-linearities in the CNNs, enabling it to learn complex relationships in the data.

3. Pooling layers - partition the feature maps into small regions to reduce the computational complexity.

4. Fully connected layers - connect every neuron from the previous layer to the neuron in the current layer.

5. Padding - adding additional pixels around the input to maintain the spatial resolution.

6. Stride - larger stride value reduces the spatial dimensions of output feature maps.

7. Skip connections - bypass one or more layers in the CNN, allowing easier optimization & training of deep network models.

What are the steps to train CNNs?

1. Dataset preparation

2. Preprocessing

3. Network Architecture Selection

4. Loss Function Selection

5. Optimization Algorithm & Hyperparameter Tuning

6. Forward Propogation

7. Loss Computation

8. Backpropogation

9. Parameter Update

10. Iterative Training

11. Validation & Model Selection

12. Testing

Which are the advanced techniques in Convolutional Neural Networks?

1. Transfer Learning & Fine Tuning

2. Data Augmentation

3. Batch Normalization

4. Dropout

5. Visualizing & Interpreting CNNs

6. Object Localization & Detection

7. Adverserial Training

8. Neural Architecture Search

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