Introduction to Deep Learning
Introduction to Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits letters, or faces.
Figure 1: Images on ML of abstraction in DL
In a simple word deep learning is a machine learning method. it allows us to train an AI to predict outputs, given a set of inputs. both supervised and unsupervised learning can be used to train the AI.
What is the main difference between AI ML and DL?
Table 1: Diff. AI ML and DL
|Diff. on the Basis of||Artificial Intelligence||Machine Learning||Deep Learning|
|Introduce By||John McCarthy||Arthur Samuel||Igor Aizenberg|
|Aim||Increase the chances of success not accuracy.||Increase the accuracy not caring much about the success ratio.||It attains the highest rank in terms of accuracy when it is trained with a large amount of data.|
|Efficiency||Basically, the efficiency is provided by ML and DL Respectively.||Less Efficient than DL, as it can’t work for longer dimensions or a higher amount of data.||More powerful than ML as it can easily work for larger sets of data.|
|Ex. Of it’s
How does deep learning work?
As we already define Deep learning networks learn by discovering intricate structures in the data they experience. by building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.
Figure 2: AI ML DL
Why is deep learning important?
We are living in a time of unprecedented opportunity, and deep learning technology can help us achieve new breakthroughs. Deep learning has been instrumental in the discovery of exoplanets and novel drugs and the detection of diseases and subatomic particles. It is fundamentally augmenting our understanding of biology, including genomics, proteomics, metabolomics, immunome, and more.
Future of Deep Learning according to top AI Experts
Deep learning is currently the most effective AI technology for numerous applications. However, there is still differing opinions on how capable deep learning can become. While deep learning researchers like Geoffrey Hinton believe that all problems could be solved with deep learning, there are numerous scientists who point to flaws in deep learning where remedies are not clear. With increasing interest in deep learning from the general public as well as developer and research communities, there could be breakthroughs in the field.
What is the level of interest in deep learning?
General public Interest in deep learning is continuing to increase. Reasons of this interest include deep learning’s capacity to
i.) Improve accuracy of predictions, enabling improved data driven decisions.
ii.) Learn from unstructured and unlabelled datasets, enable analysis of unstructured data. As a result of these, deep learning solutions provide operational and financial benefits to companies. In 2012, later Turing award recipient George Hinton’s team demonstrated that deep learning could provide significant accuracy benefits in common AI tasks like image recognition. After this, companies started investing into deep learning and interest in the area has exploded. Since 2017, interest in deep learning appears stable.
Deep learning helps computers to derive meaningful links from pleth data and make sense of unstructured data. Here, the mathematical algorithms are combined with a lot of data and strong hardware to be qualified information. With this method, information from digital data is automatically extracted, classified, and analyzed. As we know Deep learning more accurate than machine learning and it’s works on a large scale of data. so we should use deep learning to enhance our AI speed as well as accuracy.
Figure 6 Progress of DL
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