Deep Learning Courses

Deep learning is a branch of machine learning entirely dependent on artificial neural networks. As neural networks mimic the human brain, deep learning is also categorised as a mimic of the human brain. There is no need to program everything unambiguously when using deep learning, as it enables the systems to cluster data and accurately perform predictions to mimic the human brain.

Deep learning, in simple words, implies it is a subset of machine learning, a neural network consisting of three or more layers. These neural networks and deep learning try to mimic the human brain's behaviour, allowing it to learn from huge amounts of data. Although a neural network with a single layer can make rough predictions, other hidden layers can assist in optimising and enhancing accuracy.

Deep learning is a specific type of machine learning that attains outstanding flexibility and power by learning to exemplify the world as an encapsulated grouping of concepts. Every concept is defined considering ease of understanding. Moreover, more abstract representations are computed compared to less abstract ones.

Deep learning is a branch of machine learning that uses algorithms for data processing. It replicates the thinking process and also develops abstractions. It uses different layers of algorithms for data processing, understanding human speech, and identifying objects visually.

Beginners can comprehend deep learning as a process where information passes through each layer, and the output of the preceding layer behaves as the input for the succeeding layer. Clearing these concepts is vital if you aim to have deep learning from scratch. Now let’s understand what it is used for.

Deep learning powers several artificial intelligence applications and services capable of enhancing automation and physical and analytical tasks without human interference. It is essential to learn deep learning because this technology is responsible for powering everyday products and services (for example - voice-enabled TV remotes, digital assistants, and credit card fraud recognition) and developing technologies (like self-driving cars).

Learning deep learning from scratch helps an individual to use it for areas like medical research, automated driving, industrial automation, aerospace and defence, electronics, health care, government, marketing, and sales.

In most cases, real-life deep learning applications are so impeccably integrated into everyday products and services that we are hardly aware of the complex data processing occurring in the background.

The following section discusses deep learning usage in some prominent areas:

Financial services

A decent deep learning example can be its application in financial services. Financial institutions frequently use predictive analytics to execute algorithmic trading of stocks, evaluate business menaces for loan approvals, identify fraud, and organise client investment and credit portfolios.

AI in Marketing

Deep learning in data science is highly prevalent these days, using AI as an effective tool for customer service management. Implementing AI techniques facilitates enhanced speech recognition in call routing and call-center management. Consequently, customers benefit from a seamless experience.

The deep learning ai can be the deep learning analysis of audio that lets systems evaluate the emotional tone of a customer. In this deep learning in ai example, if the customer responds poorly to an AI chatbot, that system can redirect the conversation to human operators.

Customer service

Another great deep learning example in real life is its use in customer service. Plenty of organisations employ deep learning technology in their customer service conduits.

For example, chatbots employed in a wide range of services, applications, and customer service portals are the direct form of AI. Traditional chatbots utilise natural language and visual recognition. But, more sophisticated deep learning chatbot solutions try to determine if multiple responses to uncertain questions exist. Depending on the responses received, the deep learning chatbot attempts to answer these questions directly or pass the discussion to a human user.

Virtual assistants like Amazon Alexa, Google Assistant, or Apple's Siri further outspread the idea of a chatbot using speech recognition functionality. It develops a new method to engross users in a tailored way.

Healthcare

The usefulness of deep learning in healthcare is highly prevalent because it owns a great ability to improve healthcare facilities for patients. The healthcare industry had hugely benefited from deep learning potential right from when the images and records in the healthcare industry got digitised.

The image recognition applications can help radiologists and medical imaging specialists (in the form of deep learning in medical imaging) to study more images i less time. Moreover, deep learning requires limited data to learn from.
Law enforcement

Deep learning algorithms can study and learn from transactional data to detect large perilous patterns indicating fraudulent or illegal doings. Speech recognition, computer vision and deep learning, and many other deep learning applications can enhance the efficiency of investigative analysis. This is possible by retrieving patterns and proof from images, video and sound recordings, and documents. As a result, law enforcement can effectively analyse huge amounts of data more accurately and rapidly.

Automated Driving

Automotive researchers use deep learning to identify objects like traffic lights and stop signs automatically. Deep learning is also useful for detecting pedestrians, and thus, it aids in reducing accidents.

Deep learning can capture the images nearby in self-driven cars by processing a large amount of data. Subsequently, it determines what actions must be accomplished –turning left, turning right, or stopping. Accordingly, there will be a reduction in accidents every year.

Industrial Automation

Deep learning helps enhance the safety of workers working close to heavy machinery. It automatically detects when objects or people are within a risky distance from machines.

Aerospace and Defence

Deep learning can recognise objects from satellites that trace areas of interest and recognise safe or unsafe regions for troops.

Automatic Image Caption Generation

The concept of image captioning deep learning is prevalent these days. After you upload the image, the algorithm accordingly generates a caption. For example, if you say brown-coloured hair, it displays brown-coloured hair with a caption at the bottom of the image

Text generation

Deep learning machines are trained in the grammar and writing style of a text piece. Subsequently, a deep learning model automatically creates an entirely new text that matches the original text's writing style, grammar, and spelling.
Computer vision

Deep learning for computer vision is greatly enhanced to equip computers with high accuracy for image classification and object detection, restoration, and image segmentation deep learning.

The majority of the deep learning methods use deep neural network architectures. This is why deep learning models are commonly regarded as ‘deep neural networks’.

The term ‘deep’ in a neural network in deep learning typically relates to the number of hidden layers encased in the neural network. Usually, there are 2-3 hidden layers in traditional neural networks, but the deep network can include as many as 150.

Essentially, deep learning models are trained by implementing huge sets of characterised data and neural network architectures. These architectures and data directly study features from the data without undergoing manual feature extraction.

Artificial neural networks or deep neural networks try to imitate the human brain using a blend of data weights, inputs, and biases. These components work collectively to precisely distinguish, categorise, and define objects within the data.

In deep neural networks, multiple layers of mutually dependent nodes exist. Each layer builds upon the preceding layer to optimise the categorisation or prediction. The corresponding progression of computations through the network is termed ‘forward propagation’.

Input and output layers existing in a deep neural network are known as visible layers. In the input layer, the deep learning model inputs the data for processing. On the other hand, in the output layer, the final prediction or deep learning classification takes place. The classification in deep learning leads to accurate prediction.

Another important process in deep learning is called backpropagation. It uses algorithms, for example, gradient descent, to compute prediction errors. Subsequently, the algorithm fine-tunes the biases and weights of the function by shifting backwards over the layers in an attempt to train the model. The forward propagation and backpropagation work collectively to let a neural network in deep learning make predictions and resolve errors accordingly. With the repetitive use, the deep learning algorithm becomes progressively more precise.

The description above exemplifies the easiest kind of deep neural network. But deep learning algorithms are quite complex, and various types of neural networks are employed to solve specific problems.

For example, Convolutional neural networks (CNNs) are extensively used in deep learning for computer vision and image classification applications. They can detect patterns and features in an image and enable tasks like deep learning in object detection and recognition. Recurrent neural networks (RNNs) are widely used in natural language and speech recognition applications. The reason is that RNNs control sequential or time-series data.

Deep learning is extensively used nowadays because companies in various industries emphasise using cutting-edge computational techniques to discover useful information concealed over enormous volumes of data. Although the artificial intelligence domain is now decades old, innovations in artificial neural networks empower the eruption of deep learning.

The fields of AI and Deep Learning got a significant impetus because computers are getting closer to conveying human-level capabilities. Nowadays, consumers are overwhelmed with an assortment of chatbots such as Amazon‘s Alexa, Apple‘s Siri, and Microsoft‘s Cortana that employ natural language processing and machine learning to answer questions.

Currently, companies of all industries are targeting to present their big data deep learning sets as a training platform for developing sharper AI programs. These programs can extract valuable information and interrelate it with the world more naturally.

As per researchers, certain components lay the foundation for developing smart, self-learning machines and will begin rivalling humans in their insight. These components are cutting-edge neural networks, outstandingly powerful distributed GPU-based systems and the availability of huge volumes of training data.

Deep Learning algorithms are widely used in manufacturing industries because they transform complex, time-consuming, expensive manufacturing processes into easy-to-understand, quick, and cost-effective ones. Moreover, Deep learning instils problem-solving ability in manufacturers. This ability can surpass conventional machine vision applications. Deep Learning achieves this with excellent reliability and strength.

Deep learning software optimised for factory automation empowers companies in several industries to develop innovative inspection systems. These systems drive the boundaries of machine vision and outline the future of industrial automation. Furthermore, these cutting-edge inspection systems blend the reliability and efficiency of a computerised system with the flexibility of human visual inspection.

By going through these three key points, you can understand why Deep Learning is used and why it's so popular. These points justify how Deep Learning is superior to classical machine learning algorithms:

• Deep learning models are independent of manual feature extraction. Instead, they can learn purposeful representations of the assigned features, called Representation Learning. Specifically, it is useful when handling non-trivial tasks wherein selecting an instructive subset of features can be very challenging.
• In the case of ample data and reasonable computational competence, the Deep Learning models usually perform better than traditional machine learning algorithms. The reason is they can signify a much wider set of functions.
• Certain Deep Learning architectures, for example, Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), benefit from semantic knowledge in their architecture. This makes them predominantly competent in a group of tasks. For example, CNNs are influenced by the visual cortex.

Few more points justifying why to use Deep Learning:

• Deep Learning surpasses other learning approaches if the data size is big.
• In the absence of domain adaptation deep learning for feature introspection, Deep Learning methods surpass traditional learning techniques because they have to concern less regarding feature engineering.
• Deep Learning algorithms can solve complex problems related to natural language processing, image classification, and speech recognition.

Deep learning is a subdivision of machine learning. It uses algorithms for data processing, mimics the thinking process, and creates abstractions. Moreover, it uses layers of algorithms for data processing, understanding human speech, and recognising objects visually. Exploring its great features benefits myriad industries, but it is significant to know the associated history that illustrates how it's gradually developed. The below section illustrates Deep Learning history:

Development of Deep Learning Algorithms

The initial efforts in developing deep learning algorithms trace back to 1965. In that year, Valentin Grigorʹevich Lapa and Alexey Grigoryevich Ivakhnenko used models with polynomial activation functions and successively analysed these functions.

The 1970s-era marked a momentary impediment to the development of AI. During this time, restrictions were put on research in areas like artificial intelligence and deep learning due to a lack of funding. But, individuals continued the research without funding through those challenging years.

Kunihiko Fukushima was the first to use the convolutional neural network in deep learning, designing the deep learning neural networks with convolutional layers and multiple pooling. Moreover, in 1979, he developed an artificial neural network called Neocognitron. This network utilised a hierarchical and multi-layered design. This design facilitated the computer to learn to identify visual patterns. Furthermore, the networks simulated modern versions and were skilled with a reinforcement tactic of recurring activation in the multiple layers. These networks become more robust with time.

Development of the FORTRAN code for Back Propagation

The 1970s further saw a significant growth of backpropagation. It utilises errors in training the deep learning models. The concept of backpropagation gained popularity when Seppo Linnainmaa completed his master’s thesis, incorporating a FORTRAN code for backpropagation. Even though being developed in the 1970s, this concept was not deployed to neural networks till 1985. In 1985, Hinton and Rumelhart Williams validated the backpropagation concept in a neural network. This validation lets backpropagation offer remarkable distribution representations.

Yann LeCun enlightened the foremost practical manifestation of backpropagation, in 1989, at Bell Labs. This was done using a deep convolutional neural network and backpropagation to read the transcribed digits. Moreover, this combination of a backpropagation system and convolutional neural network in deep learning was adopted to read the numerals of transcribed checks.

The 1985-90s era perceived another hiatus in artificial intelligence. Consequently, it impacted the research of deep learning neural networks. Subsequently, in 1995, Dana Cortes and Vladimir Vapnik built the support vector machine. This machine was able to map and identify similar data. Sepp Hochreiter and Juergen Schmidhuber developed LSTM (Long short-term memory) in 1997. It proved useful for recurrent neural networks.

The year 1999 marked the next remarkable advancement in deep learning advancement. This year, computers used the speed of GPU processing. The advancement in deep learning led to quicker processing which increased the computational speeds by 1000 times across ten years. In that era, neural networks started competing with support vector machines. Neural networks used the same data as a support vector machine and offered better results but with slow speed.

Development of Deep Learning in the 2000s and beyond

The Vanishing Gradient Problem was announced in 2000 when the upper layers could not learn the “features” (lessons) created in the lower layers due to the absence of a learning signal in these layers. It was not a major problem for every neural network. However, it is limited to gradient-based learning methods only. Moreover, this problem resulted in some activation functions that compressed their input and decreased the output range in a disordered way. Consequently, large areas of input were mapped to an enormously small range.

In 2001, META Group (presently called Gartner) presented a consolidated research report. This report demonstrated the opportunities and challenges of three-dimensional data growth. This report depicted the blitz of big data deep learning and demonstrated the increasing speed and volume of data with increasing the range of data types and sources.

In 2009, Fei-Fei Li (an AI professor at Stanford) launched ImageNet. It assembled a free database of 14+ million labelled images. All these images worked as the inputs to train the neural nets. By the year 2011, there was a significant increase in the speed of GPUs. This speed was adequate to train convolutional neural networks without requiring pre-training on each layer. So, by 2011, Deep learning showed noteworthy advantages in terms of speed and efficiency.

The Cat Experiment

In 2012, Google Brain launched the results of an atypical independent project known as ‘The Cat Experiment’. This project discovered the challenges of unsupervised learning. On the other hand, Deep learning sets up supervised learning that implies the convolutional neural network is trained through labelled data similar to the images from ImageNet.

The Cat Experiment used a neural network across 1,000 computers where 10 million untagged images (without labels) were randomly taken from YouTube. These images were taken for feeding inputs into the training software. From 2012 onwards, unsupervised learning remains a major goal in Deep Learning.

The year 2018 and beyond depicted the evolution of artificial intelligence. This evolution will be based on Deep Learning. Note that Deep learning is still in its growth phase. It constantly needs innovative ideas to advance further.