Deep Learning
What is Deep Learning?
Deep learning is a subset of machine learning (ML), which is also a subset of artificial intelligence (AI). Deep learning is a technique for teaching machines to perform tasks that only humans can perform.
The difference between machine learning and deep learning is that machine learning uses simple techniques to perform tasks, whereas deep learning uses artificial neural networks to perform tasks.
With advanced technology, neural networks are no longer limited and can observe, learn, and take actions on their own, often faster and more accurately than humans.
Deep learning is being used in a variety of applications without the need for human intervention. It is the fastest growing field, a large number of complex tasks are being performed by deep neural networks, by learning from texts, pictures, or speech.
The number of hidden layers in the neural network is referred to as “deep.” Deep neural networks can have up to 150 hidden layers, whereas traditional neural networks only have two to three.
The number of layers in the network indicates how deeper it is. Just like the human brain is made up of neurons similarly neural networks are layers of nodes.
Individual layer nodes are connected to nodes in adjacent layers. In the human brain, a single neuron receives thousands of signals from other neurons whereas, in an artificial neural network, signals travel between nodes, assigning corresponding weights.
A node with a higher weight will have a greater impact on the nodes below it. The final layer assembles the weighted inputs into a result.
Deep Learning Models:
Deep learning models are taught to utilize massive amounts of labeled data and neural network topologies that learn features directly from the data which eliminates the requirement for manual feature extraction.
These systems necessitate powerful hardware since they process a vast amount of data and perform multiple difficult mathematical calculations. Deep learning training computations, even with such advanced technology, can take weeks. Also, the information must be fed in large amounts in order to get accurate results in deep learning.
You must have a high GPU performance in order to get accurate results in deep learning because it requires a large amount of data and it is more complicated than machine learning. During the rise of machine learning, scientists largely ignored neural networks.
A machine learning algorithm, called Adaboost was developed in 2001 to detect faces in images in real-time. It filtered images using decision sets. The probability of selecting the correct face from an image increased as the data moved further down the decision tree.
Neural networks started gaining importance when powerful graphics processing was used to process images on desktop computers which required a large amount of labeled data with ImageNet, a database of millions of labeled images from the Internet.
Crowdsourcing has replaced the time-consuming task of manually labeling images, providing networks with a virtually limitless source of training materials.
Why you should learn about Deep learning?
Now is the best time to learn about deep learning because this new technology is becoming popular day by day and it has a bright future. Deep learning has many applications which are discussed below:
-Deep learning is used in the field of aerospace to identify objects from satellites. It also indicates whether an area is safe or dangerous for troops.
-In medical research, deep learning models are playing an important role. It is being used by cancer researchers to detect cancer cells automatically. They have created an improved microscope that generates a high-dimensional data collection that can be utilized to train a deep learning program to identify cancer cells properly.
-Deep learning is being used by automotive researchers to automatically detect objects like traffic lights and stop signs which uses complex layers of neural networks. It is also used to detect pedestrians, which helps to reduce accidents.
-Deep learning is assisting in the improvement of worker safety. It automatically detects when people or objects are within an unsafe distance from machines.
-Deep learning is used in automated hearing and speech translation in electronics. Home assistance devices that respond to your voice and remember your preferences are an example of it.
The National Academy of Sciences hosted a Sackler Colloquium on “The Science of Deep Learning” in March 2019 in the Academy building in Washington, DC. The goal was to advance the scientific understanding of today’s empirically derived deep-learning systems while also advancing the use of these systems in traditional scientific research.
Development and Training of Deep Learning Models:
You might be wondering how these deep learning models are created and trained. Here are the most common methods for performing object classification using deep learning.
A large amount of labeled data set is required to design a network architecture that would be able to learn the features. This works great for new applications and it is also beneficial for applications with a large number of output categories. These networks typically take days or weeks to train due to the large amount of data and rate of learning. This less commonly used method is generally called “Training from scratch”.
So, the transfer learning approach (which is another method of creating or training a deep learning model) is used in the majority of deep learning applications, which requires fine-tuning a pre-trained model. By beginning with an existing network, such as Google Net, new data is fed with previously unknown classes and you can perform a new task, such as categorizing only dogs or cats rather than 1000 different objects after a few network tweaks. This has the additional benefit of using far less data and decreasing calculation time to minutes or hours.
Using the network as a feature extractor is a slightly less common method, but a more specialized approach to deep learning. Because all of the layers are tasked with learning specific features from images, we can extract these features from the network at any time during the training process, then these characteristics can be fed into a machine learning model, such as a support vector machine (SVM).
In order to simplify deep learning, MATLAB makes it easy by providing tools and functions for managing large data sets, as well as specialized toolboxes for machine learning, neural networks, computer vision, and automated driving. It also allows you to do deep learning without being an expert with just a few lines of code.
Benefits of using Deep Learning:
There are many advantages of using it that’s why it is commonly used by many engineers and scientists around the globe. MATLAB allows you to perform deep learning without becoming an expert.
For training and testing deep learning models, MATLAB allows users to label objects within images and automate ground truth labeling within videos which helps to produce better results in less time.
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Furthermore, multiple units can be combined in a single workflow using MATLAB. It also provides deep learning tools and functions such as signal processing, computer vision, and data analytics.
As we know that deep learning models learn from the data hence it is important to keep it well organized in order to build an intelligent system.
Representation of Data in Deep Learning:
Data can be represented in many forms, some of which are given below:
-Sequential data: When building a new model, the order of input data is important. It includes text streams, audio fragments, video clips, and time-series data.
-Tabular Data: There are mostly rows and columns in the Tabular data set and they contain data in a columnar format like database tables. In tabular data, each column may only contain data of the specified type and each column (field) must be named.
This overall arrangement of logical and systematic data is based on data features and properties and through tabular data, deep learning models allow us to create data-driven intelligent systems by learning more efficiently.
-Image or 2D data: The four essential characteristics or fundamental parameters of a digital image are pixels, voxels, matrix, and bit depth. A digital image is composed of a matrix, which is a rectangular array of symbols, numbers, or expressions organized in columns and rows in a two-dimensional array of numbers.
The data types mentioned above are most common in deep learning applications. Depending on the nature and characteristics of the data, various classifications of deep learning techniques perform differently.
Stages of Deep Learning Processing:
Deep learning models typically go through the same stages of processing as machine learning models. A deep learning workflow for real issues that includes three processing steps:
-Data understanding and pre-processing.
-Deep learning model building and training.
-Validation and interpretation.
Unlike ML modeling, however, feature extraction in the deep learning model is automated rather than manual. Convolution neural networks, recurrent neural networks, autoencoders, deep belief networks, and other deep learning models are examples.
Dependencies of Deep Leraning Models:
There are some dependencies and key properties of deep learning models that must be considered before beginning work on deep learning applications.
-Deep learning is typically reliant on a large amount of data in order to build a data-driven model for a specific problem domain. The reason for this is that deep learning algorithms frequently perform poorly when the data size is short.
-Deep learning algorithms necessitate large computational operations when training a model with large datasets. Because the GPU has a greater advantage over a CPU for larger computations, it is mostly used to optimize operations efficiently.
As a result, GPU hardware is required to work properly with deep learning training. As a result, deep learning is more reliant on high-performance machines with GPUs than traditional machine learning methods.
-The process of extracting features (properties, characteristics, and attributes) from raw data using domain knowledge is known as feature engineering. The attempt to retrieve elevated qualities directly from data distinguishes deep learning from other machine-learning techniques.
As a result, deep learning reduces the time and effort it takes to create a feature extractor for each trouble.
-Deep learning models can take more than a week to complete a training session because of the enormous number of parameters in deep learning, consequently, the model training process takes longer, whereas training with ML algorithms takes only seconds to hours.
When compared to other machine learning methods, deep learning algorithms run exceptionally quickly during testing.
How Pre-Trained Deep Leraning Models help?
As deep learning models require a large amount of data, it is not necessary that every time a lot of data is available so a pre-trained model for a similar source task can be used to tackle a specific target task hence transfer learning is becoming popular.
The tasks may differ, but their domains must be the same. In other words, because the input datasets are different, transfer learning between speech recognition and image classification tasks is not possible. You can anticipate cat photographs by utilizing a pre-trained image classifier trained on dog photos.
There are three basic approaches to transfer learning on deep learning models.
-The most basic method is to solve a target problem by applying a model from a source task directly. Such models are often massive (millions of parameters) neural networks that have been trained on cutting-edge equipment for days or even weeks.
Large entities (companies, institutions, etc.) frequently distribute such models to the public in order to accelerate the growth of this sector.
-Rather than using the model end-to-end as in the previous example, we may use the pre-trained neural network as a feature extractor by removing the last fully-connected output layer. This method enables us to directly apply a fresh dataset to address a completely different problem.
It enables the employment of lightweight linear models as well as the specification of the dimensions of the last fully-connected layer (e.g. Linear SVM, Logistic Regression). When the target task dataset is very tiny, the feature extraction technique is best suited.
-We can take it a step further by fine-tuning weights in some layers of the pre-trained model in addition to training the output classifier. Typically, the network’s earliest layers (particularly for CNN) are frozen, while the latter ones are opened up for tuning.
This enables us to fully train the existing model and alter the parameters at the very final layers.
Final Words:
I have tried to cover the important aspects of the vast field of Deep Learning and hope that you found this article useful. If so, then please follow me so that I can get the motivation to do more.
