Deep learning
- 1. Presented by:- Anima Singh Dhabal
- 2. Content • Artificial intelligence • Machine learning • Data science • Deep learning • How deep learning works • Deep learning requirements • Difference between AI, ML, DL • ML Vs. DL • Fruit detection system using deep neural network • Block diagram of fruit detection system using deep learning • Conclusion
- 3. Is Artificial Intelligence, Machine Learning and Deep Learning the same thing?
- 5. Artificial Intelligence • AI is any technique, code or algorithm that enables machine to develop, demonstrate and mimic human cognitive behaviour or intelligence and hence the name “Artificial Intelligence”. • AI doesn’t mean that everything machines will be doing, rather AI can be better represented as “Augmented Intelligence” i.e., Man-Machine to solve business problems better and faster. • AI won’t replace managers, but managers who use AI will replace those who don’t. • Some of the most successful applications of AI around us can be seen in robotics, computer vision, virtual reality, speech recognition, automation, gaming and so on..
- 6. Machine Learning • Machine Learning is the sub field of AI, which gives machines the ability to improve its performance over time without explicit intervention or help from the human being. • In this approach, machines are shown thousands or millions of examples and trained how to correctly solve a problem. • Most of the current applications of the machine learning leverage supervised learning. • Other usage of ML can be broadly classified between unsupervised learning and reinforced learning.
- 7. Data Science • Data Science is a field which intersects AI, Machine Learning and Deep Learning and enables statistically driven decision making. • Data Science is the art and science of drawing actionable insights from the data. • Data Science + Business Knowledge + Impact/Value creation for the Business. • Generally speaking, Data Scientists and analytics professionals try to answer following questions via their analysis: • Descriptive analytics( what has happened?) • Diagnostic analytics(why it has happened?) • Predictive analytics(what may happen in future?)
- 8. Deep Learning • Deep learning is a subfield of machine learning that very closely tries to mimic human brain’s working using neurons. • These techniques focus on building artificial neural networks(ANN) using several hidden layers. • There are variety of deep learning networks such as multilayer perceptron(MLP), Auto encoders(AE), Convolution neural network(CNN), recurrent neural network(RNN).
- 9. Deep Learning is the fastest growing technology of AI
- 10. How Deep Learning Works • If I give you images of horses, you recognize them as horses, even if you’ve never seen that image before. And it doesn’t matter if the horse is lying on a sofa, or dressed up for Halloween as a hippo. You can recognize a horse because you know about the various elements that define a horse: shape of its muzzle, number and placement of legs, and so on. • Deep learning can do this. And it’s important for many things including autonomous vehicles. Before a car can determine its next action, it needs to know what’s around it. It must be able to recognize people, bikes, other vehicles, road signs, and more. And do so in challenging visual circumstances. Standard machine learning techniques can’t do that.
- 11. Deep Learning Requirements • Over the period of time, more drive for automation, AI( e.g.. Autonomous Car). • Some problem cannot be mathematically programmed exclusively, instead making machines learning by itself. e.g..Face recognition. • Over the period, percentage of unstructured data has grown to about 90% of total data. E.g.. Pictures, YouTube videos, twitter chats, Watsapp logs etc. Deep Learning is well suited for picture, audio and speech/language processing etc. • Highly non linear models can be fitted on Big Data without much issue of over fitting. • High capacity computational power for cheap makes the tedious calculations very possible to implement.
- 12. Difference between AI, Machine Learning, Deep Learning • The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both. • “Machine Learning is a technique of parsing data, learn from that data and then apply what they have learned to make an informed decision.” “Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way but it has different capabilities.” “AI is a ability of computer program to function like a human brain.”
- 13. • The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance. If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning. • Let’s understand it with the help of a figure:-
- 14. ML vs. DL
- 15. Fruit Detection System Using Deep Neural Networks • Fruit detection system is a system for automatic harvesting of fruits. The system was implemented to detect the matured strawberry fruits from the plants , so that the robots could plug the fruits from the plants and collect on the tray. This image processing system used matlab for image processing. We have used deep neural networks for training .The proposed algorithm works well and compared with other algorithms in terms of accuracy.
- 16. Block Diagram of Fruit detection system using Deep Learning
- 17. Conclusion Deep Learning gets its name from how it’s used to analyze “unstructured” data, or data that hasn’t been previously labelled by another source and may need definition. That requires careful analysis of what the data is, and repeated tests of that data to end up with a final, usable conclusion. Computers are not traditionally good at analyzing unstructured data like this.
- 18. THANK YOU