DL Research and Applications

Deep Learning: A Technique for Implementing Machine Learning

- Overview

Deep learning (DL) is a type of machine learning (ML) that uses artificial neural networks (ANNs) to teach computers to process data and make predictions based on complex patterns. 

DL models are trained on large amounts of data to learn to associate features in the data with the correct labels. For example, a DL model might learn to associate the shape and color of an object with the correct label, such as "dog" or "cat". 

DL can be used to automate tasks that typically require human intelligence, such as image recognition, natural language processing, and speech recognition. 

DL models use multilayered neural networks, called deep neural networks (DNNs), with three or more layers. The adjective "deep" refers to the use of multiple layers in the network. 

DL models can be trained using supervised, semi-supervised, or unsupervised learning. Unsupervised learning allows DL models to extract characteristics, features, and relationships from raw, unstructured data. 

Please refer to the following for more information:

• Wikipedia: Deep Learning

- Deep Learning and Multi-layered ANNs

Deep Learning (DL) is a subset of Machine Learning (ML), which is a subset of Artificial Intelligence AI). DL is just a type of ML, inspired by the structure of a human brain. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

DL algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, DL uses a multi-layered structure of algorithms called neural networks.  

DL uses ANNs to learn things. There are three types of ANNs: input layer, hidden layer, and output layer. DL is said to be deep because of its hidden layers. Before DL, in neural networks, it was difficult to solve a complex problem with only two layers. In DL, there are multiple hidden layers that form a larger network. It has the potential to solve complex problems.

Most modern DL models are based on multi-layered neural networks such as convolutional neural networks.

- Deep Neural Networks (DNNs)

A deep neural network (DNN) is a ML technique that uses artificial neural networks (ANNs) to perform complex tasks. 

DNNs have multiple layers of nodes that receive input from other layers and produce an output. The more layers, the more complex the network. 

DNNs are trained on large amounts of data to identify patterns, classify phenomena, and make predictions. DNNs work best with GPU-based architectures for faster training times. DNNs learn by adjusting weights to minimize error between the network's output and the desired outcome. 

DNNs are more complex and resource-intensive than conventional neural networks because of their multiple hidden layers. The hidden layers store information about the importance of inputs and make associations between them.

- Deep Learning, Neurons, and the Human Brain

Neural networks are based on the principles of the human brain. A neural network consists of many neurons and connections between them. A neuron can be represented as a function with several inputs and one output. 

Each neuron takes parameters from inputs (each input may have a different weight, which determines its importance), performs a specific function on them and gives the result to the output. The output of one neuron can be the input for another. Thus, multi-layer neural networks are formed, which are the subject of deep learning (DL).  

DL is ML. More specifically, DL is considered an evolution of ML. It uses a programmable neural network that enables machines to make accurate decisions without help from humans. 

DL achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.

DL software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data.

The basic idea - that software can simulate the neocortex’s large array of neurons in an artificial “neural network” - is decades old, and it has led to as many disappointments as breakthroughs. 

But because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before.

- Deep Learning Architectures

As machine learning (ML) becomes more advanced, it has given rise to deep learning (DL), the ability of machines to learn automatically without any human intervention. 

DL algorithms mimic the human brain and often provide the experience of interacting with a real person. The architecture of a DL model typically consists of several interconnected layers, each serving a specific purpose in processing and transforming input data to generate useful outputs. 

While DL is certainly not new, it is experiencing explosive growth because of the intersection of deeply layered neural networks and the use of GPUs to accelerate their execution. 

Big data has also fed this growth. Because DL relies on training neural networks with example data and rewarding them based on their success, the more data, the better to build these DL structures.

The number of architectures and algorithms that are used in DL is wide and varied. Artificial neural network (ANN) is the underlying architecture behind DL. Based on ANN, several variations of the algorithms have been invented.

DL models, including various architectures like recurrent neural networks (RNN), convolutional neural networks (CNN), and deep belief networks (DBN), are structured in a specific manner to enable learning from complex data and making predictions or classifications. 

- Deep Learning Algorithms

Deep learning (DL) algorithms are at the forefront of artificial intelligence (AI). DL algorithms are computer programs that mimic the human brain to learn and perform complex tasks. 

DL algorithms are made up of layers of interconnected nodes that process data. The algorithm learns by associating features in the data with labels, and then uses this knowledge to make predictions on new data. 

DL algorithms are typically trained on large datasets of labeled data. During training, the algorithm adjusts the connection strengths between layers to minimize the error between predictions and the true labels. 

DL algorithms are used to automate tasks that typically require human intelligence, such as language translation and image recognition. 

Chatbots, media recommendation features, social media feeds, and predictive text are all examples of machine learning, a branch of artificial intelligence that allows computer systems to learn without human intervention.

[Stanford University]

- Types of Deep Learning Algorithms

DL algorithms have improved over the past few decades and developers around the world have contributed to open source frameworks like TensorFlow, Theano, Keras, and Torch, all of which make it easy for people to build DL algorithms as if playing with LEGO pieces. 

And thanks to the demand from gamers around the world, GPUs (graphics processing units) make it possible for us to leverage DL algorithms to build and train models with impressive results in a time-efficient manner.

There are many types of DL algorithms, for example:

• Convolutional Neural Network (CNN): A type of deep learning algorithm that's well-suited for image recognition and processing.
• Deep reinforcement learning: Algorithms like Deep Q networks and Deep Deterministic Policy Gradient (DDPG) are used to reinforce tasks like robotics and game playing.

- Deep Learning with Big Data on GPUs

The AI boom took off when people realized that they can utilize GPU technology to train DL models much faster than waiting days for a general-purpose CPU to complete one cycle of model training. 

Since 2016, when GPU manufacturers like NVIDIA, Intel, and others created their first AI-optimized GPUs, most AI development has been related to how to train models to be as accurate and predictive as possible.

DL is a type of ML that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, DL sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.

DL is probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this fancy field. If you think big data is important, then you should care about DL. The Economist says that data is the new oil in the 21st Century. If data is the crude oil, databases and data warehouses are the drilling rigs that digs and pumps the data on the Internet, then think of DL as the oil refinery that finally turns crude oil into all the useful and insightful final products. 

There could be a lot of “fossil fuels” hidden underground, and there are a lot of drills and pumps in the market, but without the right refinery tools, you ain’t gonna get anything valuable. That’s why DL is important. It’s part of the data-driven big picture.

The good news is, we are not going to run out of data and our “refinery machine” is getting better and better. Today, just about doing anything online will generate data. In the meantime, as long as the data isn’t garbage-in, then there’s no garbage-out from DL. Also, this “oil refinery” is improving on both software and hardware.