Abstract
Deep learning is one of the latest trends in machine learning and artificial intelligence research. It is also one of the most popular scientific research trends today. Deep learning methods have brought revolutionary advances to computer vision and machine learning. New deep learning technologies are constantly emerging, surpassing the state-of-the-art machine learning and even existing deep learning techniques. In recent years, significant breakthroughs have been made in this field worldwide.
The term “deep learning” (DL) was first introduced in machine learning (ML) in 1986 and later used in artificial neural networks (ANN) in 2000. Deep learning methods consist of multiple layers to learn data features with multiple levels of abstraction. The first step in understanding how deep learning works is to grasp the distinctions between the following key terms.
Differences Between AI and ML, Supervised Learning and Unsupervised Learning
AI vs. ML
Artificial Intelligence (AI) is the replication of human intelligence in computers.
When AI research first began, researchers attempted to replicate human intelligence to accomplish specific tasks, such as playing games.
They established numerous rules for the computer. The computer had a specific list of possible behaviors and made decisions based on the established rules.
Machine Learning (ML) refers to the ability of machines to learn from large datasets (rather than fixed rules).
Machine learning allows computers to learn autonomously. This type of learning leverages the processing power of modern computers to easily handle large datasets.
Supervised Learning vs. Unsupervised Learning
Supervised learning uses labeled datasets with inputs and expected outputs.
If you train AI using supervised learning, you provide it with an input and tell it the expected output.
If the output produced by the AI is incorrect, it will adjust its calculations. This process is completed by traversing the dataset until the AI stops making mistakes.
An example of supervised learning is a weather forecasting AI. It learns to predict the weather using historical data. The training data includes inputs (air pressure, humidity, wind speed) and corresponding outputs (temperature).
Unsupervised learning is machine learning using datasets without specific structures.
If you train AI using unsupervised learning, you let the AI logically classify the data.
An example of unsupervised learning is an e-commerce website’s behavior prediction AI. It does not learn through labeled datasets.
Instead, it creates classifications of input data on its own. It tells you which users are most likely to purchase a product.
Deep learning is an important branch of machine learning, and it is achieved through algorithms that simulate the functioning of human brain neurons, achieving significant results in recent years.
So, how does deep learning work? You will have to watch this video to find out!
Broadly defined, deep learning is an overlay of existing machine learning methods.
In imaginative terms, deep learning uses systems similar to neural networks to learn.
Exciting Benefits: The AI Science Series Course is Live
As the world’s first and one of the few global companies capable of providing enterprise-level artificial intelligence deep learning JAVA open-source platforms (Deeplearning4j) and enterprise-level AI solutions, Skymind is publicly promoting the online AI science series course “Decoding Artificial Intelligence and Deep Learning”. In this series, Skymind’s senior engineer, Teacher Jingzhi, will explain the applications of deep learning in various fields and help everyone understand the mysteries of “artificial intelligence” and deep learning!
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Update Time:
Every Friday AM
Organizer:
Mozi Salon & Skymind
Course Highlights:
The science popularization course consists of four sessions, gradually decoding artificial intelligence and deep learning, introducing artificial intelligence and deep learning in the simplest way, as well as how deep learning works. Each session can be divided into 2-3 thematic videos, with one theme updated each week. The video content is concise, unique, and utilizes “fragmented time” to share cutting-edge AI technology, allowing people to feel the infinite charm of AI.
Moreover, it can quickly enhance users’ knowledge and skills in cognition and practice, enabling them to deeply explore and solve the current challenges encountered during deployment.
Course Summary:
01
Decoding Artificial Intelligence and Deep Learning
The game between human wisdom and human intelligence
How deep learning networks work
Machine learning and learning tasks
02
In-depth Exploration of Neural Networks
Introducing the operation and learning methods of neural networks using feedforward neural networks
Introducing forward propagation and backward propagation
03
Deep Learning Enables Computers to See
Introducing the structure of Convolutional Neural Networks (CNN)
Applications of deep learning in machine vision
04
How to Equip Deep Learning with Memory Mechanisms
Understanding Recurrent Neural Networks
Introducing Long Short-Term Memory Networks (LSTM)
Applications of Long Short-Term Memory Networks
This Course: How Deep Learning Networks Work
Instructor Introduction:
Leonard Loh
Senior Instructor
Master’s from Universiti Sains Malaysia
Senior Deep Learning Engineer at Skymind
Senior Instructor for Skymind’s Deep Learning Commercial Training Course
His research focuses on machine vision and signal processing of sensors. He has developed deep learning-based applications in the field of machine vision and sensors, designed and utilized machine vision technology for automated product quality inspection, and has developed applications for different business domains.
Skymind|Provider of AI Infrastructure
Surprise Benefits
For any questions regarding the course, you can leave a message in the WeChat backend or join the group for communication. The fifth session will have a special Q&A segment. Everyone, study hard and improve every day!
For those with experience in deep learning employment and teaching, you can join our instructor team. Everyone participating in the competition will receive a copy of “Deep Learning Basics and Practice” and there are generous rewards waiting for you (details can be consulted with staff in the group).
Skymind
Do you want to explore deep learning knowledge with more like-minded individuals and receive thoughtful services for exchanging AI industry knowledge with experts, and timely updates on the latest technology in the AI industry?
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Exciting Reading
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【智者见“智”】AI Science Series: Decoding Artificial Intelligence and Deep Learning
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“I, Artificial Intelligence” Column | Definition Part III: The Mysterious “Intelligence”?
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The Mozi Salon is a large public forum for science popularization organized by the Shanghai Institute of the University of Science and Technology of China, co-organized by the Science and Technology Association of Pudong New Area and the Alumni Foundation of the University of Science and Technology of China. The salon’s target audience is the general public with a strong interest in science and a love for popular science, aiming to create a popular science forum that middle school students can understand the most cutting-edge scientific information globally.
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