Deep Learning has facilitated multiple approaches to computer vision, cognitive computation and refined processing of visual data. One such instance is the use of CNN or Convolutional Neural Networks for object or image classification. CNN algorithms provide a massive advantage in visual-based classification by enabling machines to perceive the world around them (in the form of pixels) as humans do.
CNN is fundamentally a recognition algorithm that allows machines to become trained enough to process, classify or identify a multitude of parameters from visual data through layers. This promotes advanced object identification and image classification by enabling machines or software to accurately identify the required objects from input data.
CNN-based systems learn from image-based training data and can classify future input images or visual data on the basis of its training model. As long as the dataset that is used for training contains a range of useful visual cues (spatial data), the image or object classifier will be highly accurate.
CNN is one of the most popular deep learning approaches being used today in popular implementations such as the image classification system of Google Lens or in autonomous vehicles like Teslas. This is especially due to reliable pattern recognition that is possible with the help of CNN, besides the detection of objects.
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Applications of CNN
The use of CNN-based systems can be seen in security systems, defence systems, medical diagnostics, image analysis, media classification and other recognition software. For example, CNN can be used with RNN (Recurrent Neural Network) to build video recognition software or action recognisers.
This is a more advanced application of video classification that can allow systems to identify objects in real-time from videos by analysing the spatial information available in the frames that sequentially form the video.
The sequence of these frames also contains temporal information that helps model the data through spatial and temporal processing, allowing the use of a hybrid architecture consisting of both convolutions and recurrent layers. Tesla cars and Waymo vehicles use CNN to recognise and classify different aspects of roads and the incoming objects or vehicles with the help of data that is captured by cameras in real-time.
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Neural networks empower vehicle systems with line detection, environment segmentation, navigation and automated driving. These abilities allow autonomous cars to make complex decisions based on classification patterns such as avoiding objects, changing lanes, speeding up, slowing down or completely halting by braking if required.
However, these are more advanced implementations of CNN that require hardware and sensors such as GPS, RADAR, LiDAR as well as massive amounts of training data and high-performance processing environments. These help the deep learning models become decision-making systems that process the incoming data from sensors in real-time and take relevant action.
Using the data from sensors, the camera vision also procures a 3D perception of the environment (visual reconstruction, depth analysis etc.) and can analyse the distance accurately (through lasers). Thus, the model can predict the future position of vehicles or objects, finally deciding on the best course of action.
CNN models rely on classification, segmentation, localisation and then build predictions. This allows these cars to almost react like human brains would in any given situation or sometimes even more effectively than human drivers.
CNN is truly bridging the gap between machines and humans, especially when it comes to computer vision and target detection. However, to understand CNNs, we must first learn about neural networks and begin with using CNN algorithms for two-dimensional visual data.
What is a Neural Network in Deep Learning?
Deep Learning is one of the most important branches of Machine Learning and uses ANNs or Artificial Neural Networks (ANNs) to be implemented as a supervised, unsupervised or semi-supervised Machine Learning methodology. These types of Machine Learning models rely on multiple layers of processing in order to work on higher-level features in data.
Layers are fundamentally multiple nodes or blocks that are stacked together as computational units. These layers effectively emulate human neurons and function in the same manner as the human brain. By progressively building layers, a model can become much more advanced than the initial input layer that contained only pre-processed data.
Neural network algorithms extract output that can feed computations to the future layers till the final output layer is reached. This forms a network where all the nodes from every succeeding layer are connected to a single node from the preceding layer. Whenever models are using more than two layers, it is classified as Deep Neural Networks (DNNs). These networks do not form a cycle and allow multiple layers of perception, thus introducing various dimensions to predictions and data processing as well.
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Here are some common frameworks used for Deep Learning:
- TensorFlow
- Keras
- Apache MXNet
What is a Convolutional Neural Network?
Convolutional Neural Networks are a type of ANNs that are used mainly for working on pixel data to process images or for image recognition. CNNs are used in Deep Learning for generative and descriptive tasks that use machine vision and recommendation-based systems.
CNN is a more efficient ANN similar to DNNs but still reduces the complexities of a Feedforward Neural Network. This is because CNN generally relies on two layers, the feature map layer and the feature extraction layer. The input of each node extracts the local feature from the preceding layer’s local receptive field.
The positional relationship between the local and other features is plotted or mapped once the extraction is completed. To make the final resolution more accurate, the convolution layers are followed by computing layers that calculate local averages and secondary extraction of features. Even though CNNs mostly work with two layers, the predictions are extremely accurate due to the incorporation of multi-feature extraction and invariance distortion.
Nodes in the same feature map plane can learn concurrently due to having shared weights. This reduces complexities in the network and allows the entry of multi-dimensional input images. Unlike other neural networks, CNNs do not require images to get transformed into lower resolution images as processing requirements are low.
This model is similar to multilayer perceptions, except CNNs are not prone to overfitting of data, thus making them less complex. This is done through regularising the multilayer perceptron approach through penalising parameters or trimming skipped connections.
CNNs use the hierarchical pattern in data for assembling patterns by their level of complexity. Convolutional Neural Networks barely require any pre-processing compared to other classification algorithms, especially for images and video. Using NLP, one can even use CNNs for more advanced applications in robotics, medical diagnostics and automation. CNNs work great with most unsupervised machine learning techniques and independently keep optimising the model filters through automated learning methodologies.
Here are some available architectures of CNNs
- GoogLeNet
- AlexNet
- LeNet
- ZFNet
- ResNet
- VGGNet
Here is an example of a CNN implementation
Let us assume that we have to classify birds, cats, dogs, cars and humans from a random set of images. To start, we must first find a training data set that can be used as a benchmark for future computations. An example of a good training data set would be a dataset of 50,000 64×64-pixels pictures of birds, cats, dogs, cars and humans.
Each of these targets will become class labels with associated integer values. The class labels will be ‘birds’, ‘cats’, ‘dogs’, ‘cars’ and ‘humans’, having values of 0, 1, 2, 3 and 4. Once the CNN model is trained using this dataset and the benchmarks, it will be able to identify visual cues from random input data and then classify them according to their labels. The final model can accurately identify the five different types of objects (labels) from a random set of images featuring these objects.
Here are the necessary steps for building a CNN model
- Loading the dataset.
- Preparing the pixel data.
- Defining the model.
- Evaluating the model.
- Presenting the results.
- Complete Sampling.
- Develop a baseline model.
- Implement regularisation techniques for improving the model.
- Augmenting data.
- Finalising model and further evaluation.
CNN Deep Learning is a promising field with excellent career prospects. If you are planning to build a career in CNN you can check out upGrad’s Advanced Certificate Programme in Machine Learning & Deep Learning program.
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