딥 러닝
Pythagoras0 (토론 | 기여)님의 2020년 12월 22일 (화) 21:54 판
introduction
- Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems.
- image classification and labeling
- face recognition
- gesture recognition
- video search and analytics
- speech recognition and translation
- recommendation engines
- indexing and search
memo
- Kristinn R. Thórisson, Jordi Bieger, Thröstur Thorarensen, Jóna S. Sigurðardóttir, Bas R. Steunebrink, Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like, arXiv:1604.04660 [cs.AI], April 15 2016, http://arxiv.org/abs/1604.04660
노트
- This article explains deep learning vs. machine learning and how they fit into the broader category of artificial intelligence.[1]
- Deep learning is a subset of machine learning that's based on artificial neural networks.[1]
- Now that you have the overview of machine learning vs. deep learning, let's compare the two techniques.[1]
- For this reason, deep learning is rapidly transforming many industries, including healthcare, energy, finance, and transportation.[1]
- Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.[2]
- Today, deep learning enables farmers to deploy equipment that can see and differentiate between crop plants and weeds.[3]
- For example, deep learning can be as effective as a dermatologist in classifying skin cancers, if not more so.[3]
- Deep learning is based on representation learning.[4]
- Deep learning doesn’t rely on human expertise as much as traditional machine learning.[4]
- DL allows us to make discoveries in data even when the developers are not sure what they are trying to find.[4]
- All major commercial speech recognition systems (like Microsoft Cortana, Alexa, Google Assistant, Apple Siri) are based on deep learning.[4]
- Getting started in deep learning – and adopting an organized, sustainable, and reproducible workflow – can be challenging.[5]
- In the field of deep learning, there continues to be a deluge of research and new papers published daily.[5]
- NLP and deep learning continue to advance, nearly on a daily basis.[5]
- Natural language processing has made incredible advances through advanced techniques in deep learning.[5]
- Let's begin to learn what is Deep Learning, and its various aspects.[6]
- Deep learning can be considered as a subset of machine learning.[6]
- Deep learning has aided image classification, language translation, speech recognition.[6]
- Artificial neural networks, comprising many layers, drive deep learning.[6]
- The clearest explanation of deep learning I have come across...[7]
- About the book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.[7]
- Deep learning is one of the most influential and fastest growing fields in artificial intelligence.[8]
- Deep learning is an important element of data science, which includes statistics and predictive modeling.[9]
- At its simplest, deep learning can be thought of as a way to automate predictive analytics.[9]
- To understand deep learning, imagine a toddler whose first word is dog.[9]
- As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking.[9]
- Course material loosely follows the organization of the text: I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016.[10]
- Deep learning is making a big impact across industries.[11]
- Businesses often outsource the development of deep learning.[11]
- You can get started with deep learning for free with IBM Watson Studio and Watson Machine Learning.[11]
- You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow.[12]
- The number of publications addressing deep learning as applied to medical imaging techniques is a small fraction of this number.[13]
- Novel denoising algorithms based on deep learning have been studied intensively and showed impressive potential (29).[13]
- Using a combination of q-Space deep learning and of simultaneous multi-slice imaging, Golkov et al.[13]
- Manjón and Coupe (59) used two-stage strategy with deep learning for noise reduction.[13]
- This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers.[14]
- In most discussions, deep learning means using deep neural networks.[15]
- I mentioned that deep learning is a form of machine learning.[15]
- There are many examples of problems that currently require deep learning to produce the best models.[15]
- As I mentioned earlier, most deep learning is done with deep neural networks.[15]
- For example, deep learning is used to classify images, recognize speech, detect objects and describe content.[16]
- And those differences should be known—examples of machine learning and deep learning are everywhere.[17]
- More specifically, deep learning is considered an evolution of machine learning.[17]
- And as deep learning becomes more refined, we’ll see even more advanced applications of artificial intelligence in customer service.[17]
- That’s a widely shared sentiment among AI practitioners, any of whom can easily rattle off a long list of deep learning’s drawbacks.[18]
- Until the past year or so, he says, “there had been a feeling that deep learning was magic.[18]
- “So I have a hard time imagining that deep learning will go away at this point,” Cox says.[18]
- Unfortunately, neither of these milestones solved the fundamental problems of deep learning.[18]
- Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights.[19]
- Cancer researchers are using deep learning to automatically detect cancer cells.[19]
- Deep learning is being used in automated hearing and speech translation.[19]
- Deep learning is commonly used across apps in computer vision, conversational AI and recommendation systems.[20]
- Computer vision apps use deep learning to gain knowledge from digital images and videos.[20]
- In early talks on deep learning, Andrew described deep learning in the context of traditional artificial neural networks.[21]
- Finally, he is clear to point out that the benefits from deep learning that we are seeing in practice come from supervised learning.[21]
- When you hear the term deep learning, just think of a large deep neural net.[21]
- He describes deep learning in terms of the algorithms ability to discover and learn good representations using feature learning.[21]
- The adjective "deep" in deep learning comes from the use of multiple layers in the network.[22]
- In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.[22]
- The word "deep" in "deep learning" refers to the number of layers through which the data is transformed.[22]
- Advances in hardware have driven renewed interest in deep learning.[22]
- Deep learning is used across all industries for a number of different tasks.[23]
소스
- ↑ 1.0 1.1 1.2 1.3 Deep learning vs. machine learning - Azure Machine Learning
- ↑ Deep Learning
- ↑ 3.0 3.1 What Is Deep Learning? - How It Works
- ↑ 4.0 4.1 4.2 4.3 A Guide to Deep Learning and Neural Networks
- ↑ 5.0 5.1 5.2 5.3 Deep Learning
- ↑ 6.0 6.1 6.2 6.3 What is Deep Learning and How Does It Works?
- ↑ 7.0 7.1 Deep Learning with Python
- ↑ What is Deep Learning?
- ↑ 9.0 9.1 9.2 9.3 What is Deep Learning and How Does it Work?
- ↑ Deep Learning
- ↑ 11.0 11.1 11.2 Deep Learning - Neural Networks and Deep Learning
- ↑ Deep Learning Professional Certificate
- ↑ 13.0 13.1 13.2 13.3 Applications of Deep Learning to Neuro-Imaging Techniques
- ↑ Intro to TensorFlow for Deep Learning
- ↑ 15.0 15.1 15.2 15.3 What is deep learning? Algorithms that mimic the human brain
- ↑ What is deep learning?
- ↑ 17.0 17.1 17.2 Deep learning vs machine learning
- ↑ 18.0 18.1 18.2 18.3 News Feature: What are the limits of deep learning?
- ↑ 19.0 19.1 19.2 What Is Deep Learning?
- ↑ 20.0 20.1 Deep Learning
- ↑ 21.0 21.1 21.2 21.3 What is Deep Learning?
- ↑ 22.0 22.1 22.2 22.3 Deep learning
- ↑ Deep Learning Definition