Deep Learning: Real cases of application and Advances
It is supposed that GPT-3, Google's language model BERT, and Amazon's Alexa are all applications of deep learning. These are not all of deep learning’s applications. It also has found applications in a wider range of industries like retails,robotics, and gaming. If you want to explore more about the key algorithms, applications, and recent advances in deep learning, this article is for you.
Definition of deep learning
Deep learning is a subfield of machine learning that uses neural networks with multiple layers to learn and extract features from data. The term "deep" refers to the multiple layers in the network, which allow it to learn complex representations of data. Deep learning algorithms are designed to automatically discover features and patterns in data, without the need for explicit programming or human intervention.
Deep learning has shown remarkable success in a wide range of applications, including computer vision, natural language processing, speech recognition, and robotics. It is based on the concept of artificial neural networks, which are inspired by the structure and function of the human brain. The neural networks in deep learning are composed of layers of interconnected nodes, with each layer processing the output of the previous layer to learn increasingly complex representations of the input data. Through a process called backpropagation, the network adjusts the strength of the connections between nodes to minimize the error between the predicted output and the true output.
Difference between Deep learning and Machine Learning
This is noted that deep learning is a subset of machine learning that involves learning features automatically from data using deep neural networks. There are some key differences between the two as followed:
Representation of Data
rely on human-designed features
earn features automatically from the raw data
Complexity of Models
can learn complex representations of data
use simpler models that are easier to interpret and less prone to overfitting
Amount of Data
larger amount of training data
smaller amount of training data
require a lot of computation power for training, especially specialized hardware like graphics processing units (GPUs) or tensor processing units (TPUs)
can be trained on standard CPUs.
Exclusive applications and real case of Deep Learning
Deep learning has shown remarkable success in computer vision tasks such as object recognition, image segmentation, image classification,etc. One real case of the application of deep learning in computer vision is the development of a deep learning model for skin cancer detection.
The Skin Cancer Detection algorithm is now available as a mobile app, which allows users to take a photo of a skin lesion and receive an instant evaluation of whether it is likely to be cancerous or not. The app has the potential to greatly improve access to skin cancer detection, particularly in areas where access to dermatologists may be limited.
Researchers from Stanford University developed a deep learning algorithm called "Skin Cancer Detection" that can accurately detect skin cancer using images of skin lesions. The algorithm was trained on a dataset of over 130,000 skin lesion images, and achieved an accuracy rate of 91% in detecting malignant melanomas.
Natural Language Processing
Deep learning has been applied to natural language processing tasks such as language translation, sentiment analysis, speech recognition, and text generation. The most popular examples in this case is the development of language models such as GPT-3 (Generative Pre-trained Transformer 3) and Google's language model BERT which has the ability to generate human-like text responses to prompts, such as answering questions, translation or writing essays.
Deep learning has been used for speech recognition and speech synthesis, enabling devices like virtual assistants and voice-controlled systems. One real case of the application of deep learning in speech recognition is the development of the speech recognition system used by Amazon's virtual assistant, Alexa. Alexa uses a deep neural network (DNN) based on convolutional and recurrent layers to recognize speech and process user commands.
The speech recognition system used by Alexa has achieved a high level of accuracy, with some studies reporting a word error rate of less than 5%. This has made it possible for users to interact with Alexa using natural language commands, such as asking for the weather, setting reminders, or controlling smart home devices.
Deep learning is a key technology in the development of autonomous vehicles, used for tasks such as object detection, lane detection, and pedestrian recognition. Let’s take the Tesla Autopilot system as an example. Tesla Autopilot uses a combination of computer vision, radar, and deep learning to enable autonomous driving features such as lane keeping, automatic braking, and adaptive cruise control.
The deep learning component of Tesla Autopilot is a neural network called "Tesla Vision", which processes the data from cameras and sensors mounted on the vehicle to recognize and interpret objects in the environment.
Deep learning has been used in robotics for tasks such as object recognition, motion planning, and autonomous navigation.
We can mention the Spot robot by Boston Dynamics as an example for this case. Spot uses a combination of sensors, computer vision, and deep learning to navigate and interact with its environment. The robot is capable of performing a range of tasks, such as inspecting construction sites, delivering packages, and even dancing.
Spot's deep learning algorithms allow it to learn from its environment and adapt to new situations. For example, the robot can recognize and avoid obstacles, map out new environments, and even learn to open doors and manipulate objects.
Deep learning has been used to generate art, music, and other creative works. For example, Kakao Entertainment — an arm of Kakao, South Korea’s do-everything tech company — is billing Mave, its artificial band, as the first K-pop group created entirely within the metaverse, using deep learning, deep fake, face swap and full 3-D production technology.
Deep learning has been applied to gaming, including improving game AI and developing games that can learn and adapt to the player's behavior. One real case of the application of deep learning in gaming is the development of AlphaGo, a computer program that was able to beat the world champion at the game of Go, one of the most complex board games in existence.
AlphaGo was developed by Google's DeepMind using a combination of deep neural networks and reinforcement learning techniques. The system was trained on a large dataset of expert-level Go games, which allowed it to learn the complex patterns and strategies of the game.
How can you start a deep learning model?
Deep learning has shown remarkable success in tasks that require complex representations of data and where large amounts of data are available, and it is expected to have a significant impact on many areas of society in the coming years.
To start deploying a deep learning model, especially those related to images, videos, and art, having well-annotated datasets is a key factor. Pixta AI, a leading provider of data sourcing and data annotation with full-compliance data through a full-package service, takes pride in offering such services. Don’t hesitate to contact us now to get a 20% discount on your first order and start your deep learning model today!