Computer Vision labratory of stud_L4 (2).pptx
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Introduction to deep learning
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Deep learning.pptx
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Deep learning.pptx
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Deep learning is a subset of machine learning. Machine learning is the science of getting computers to act without being explicitly programmed. Deep learning is a type of machine learning that uses neural networks to learn from data. Read full blog https://writeme.ai/blog/machine-learning-vs-deep-learning-difference/#artificial-intelligence-vs-machine-learning-vs-neural-networks-vs-deep-learning
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Deep learning uses neural networks with multiple hidden layers between the input and output layers to learn representations of data with multiple levels of abstraction. It can learn these representations on its own without being programmed by humans. Deep learning has achieved great success in tasks like image recognition and natural language processing. While deep learning is gaining popularity, challenges remain in applying it to new problems like understanding videos and text. Researchers hope that advances in deep learning over the next five years will allow systems to comprehend YouTube videos and tell stories about what happened.
What is deep learning and how does it work?
Deep learning is a part of machine learning, which involves the use of computer algorithms to learn, improve and evolve on its own. Deep learning may be considered similar to machine learning. However, while machine learning works with simple concepts, deep learning uses artificial neural networks, which imitate the way humans learn and think.
Deep learning short introduction
This document provides an introduction to deep learning. It begins with a refresher on machine learning, covering classification, regression, supervised learning, unsupervised learning, and reinforcement learning. It then discusses neural networks and their basic components like layers, nodes, and weights. An example of unsupervised learning is given about learning Chinese. Deep learning is introduced as using large neural networks to learn complex feature hierarchies from large amounts of data. Key aspects of deep learning covered include representation learning, layer-wise training, and using unsupervised pre-training before supervised fine-tuning. Applications and impact areas of deep learning are also mentioned.
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### Advantages of TDM
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- 1. School of Electrical Engineering and Computing Department of Computer Science and Engineering By: Worku Jifara (PhD)
- 2. Outlines • Deep Learning • What? • Models • How? • Difference with Machine Learning • Introduction to Convolutional Neural Networks
- 3. Deep Learning? • History……………………. -The study of deep learning was first theorized in the 1980s. ….about 40 years older. -In 2017/18, deep learning is one of the top 10 technology breakthrough by MIT
- 4. Deep Learning? • Deep learning is part of a broader family of machine Learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. • Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. • Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans.* • Deep learning, a powerful set of techniques for learning in neural networks. (general sense)
- 5. Why Deep Learning matters? ***Deep learning achieves recognition accuracy at higher levels than ever before. So how does deep learning attain such impressive? Why not attain good result in early 1980s? While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful:
- 6. Why Deep Learning matters? 1. Deep learning requires large amounts of labelled data.
- 7. Why Deep Learning matters? 2. Deep learning requires substantial computing power.
- 8. Examples of Deep Learning at Work • Automated Driving • Aerospace and Defense • Medical Research • Industrial Automation • Electronics
- 9. How Deep Learning Works Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks. The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.
- 10. Deep Learning? • There are some basic deep learning models/architectures: Deep neural networks, Deep belief networks, Recurrent neural networks Convolutional neural networks etc.
- 11. What's the Difference Between Machine Learning and Deep Learning? Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.
- 12. What's the Difference Between Machine Learning and Deep Learning? • Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network. • A key advantage of deep learning networks is that they often continue to improve as the size of your data increases. • In machine learning, you manually choose features and a classifier to sort images. With deep learning, feature extraction and modelling steps are automatic.
- 13. Choosing Between Machine Learning and Deep Learning -You need to consider your data and materials How to Create and Train Deep Learning Models • Training from Scratch • Transfer Learning
Editor's Notes
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