deep learning evaluation and its advantages.ppt
- 2. CONTENTS I. Introduction II. History III. Principle IV. Technology V. Working VI. Real Time Applications VII. Future Scope VIII. Advantages IX. Conclusion 2
- 3. INTRODUCTION What is Deep Learning? Deep learning is a branch of machine learning that uses data, loads and loads of data, to teach computers how to do things only humans were capable of before. For example, how do machines solve the problems of perception?
- 4. HISTORY 4 1958: Frank Rosenblatt creates the perceptron, an algorithm for pattern recognition. 1989: Scientists were able to create algorithms that used deep neural networks. 2000's: The term “deep learning” begins to gain popularity after a paper by Geoffrey Hinton. 2012: Artificial pattern- recognition algorithms achieve human-level performance on certain tasks.
- 5. PRINCIPLE Deep learning is based on the concept of artificial neural networks, or computational systems that mimic the way the human brain functions.
- 6. TECHNOLOGY Deep learning is a fast-growing field, and new architectures, variants appear every few weeks. We'll see discuss the major three: 1. Convolution Neural Network (CNN) CNNs exploit spatially-local correlation by enforcing a local connectivity pattern between neurons of adjacent layers.
- 7. TECHNOLOGY 2. Recurrent Neural Network (RNN) RNNs are called recurrent because they perform the same task for every element of a sequence, with the output being depended on the previous computations. Or RNNs have a “memory” which captures information about what has been calculated so far.
- 8. TECHNOLOGY 3. Long-Short Term Memory LSTM can learn "Very Deep Learning" tasks that require memories of events that happened thousands or even millions of discrete time steps ago.LSTM works even when there are long delays, and it can handle signals that have a mix of low and high frequency components. 8
- 9. WORKING Consider the following handwritten sequence: Most people effortlessly recognize those digits as 504192. That ease is deceptive. The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. 9
- 10. WORKING
- 11. WORKING The idea of neural network is to develop a system which can learn from these large training examples. Each neuron assigns a weighting to its input — how correct or incorrect it is relative to the task being performed. The final output is then determined by the total of those weightings A training Sample A very basic approach: Binary Classifier
- 12. REAL TIME APPLICATIONS Automatic Colorization of Black and White Images Automatically Adding Sounds To Silent Movies Automatic Handwriting Generation
- 13. REAL TIME APPLICATIONS Automatic Text Generation Automatic Image Caption Generation Automatic Game Playing
- 14. FUTURE SCOPE 1. Deep Learning will speed search for extra terrestrial life. RobERt, short for Robotic Exoplanet Recognition for Exoplanets that are beyond our solar system.
- 15. FUTURE SCOPE 2. For Astronauts, Next Steps on Journey to Space Will Be Virtual 3. Droughts and Deep Learning: Measuring Water Where It’s Scarce
- 16. ADVANTAGES 1. It does feature extraction, no need for engineering features 2. Moving towards raw features 3. Better optimization 4. A new level of noise robustness 5. Multi-task and transfer learning 6. Better Architectures
- 17. CONCLUSION The low maturity of Deep Learning and its applications such as large deep neural networks achieve the best results on speech recognition, visual object recognition and several language related task field warrants extensive future research. Nevertheless, the possibilities of deep learning in future are infinite ranging from driverless cars, to robots exploring the universe and to what not if the upcoming architectures are creative enough.
- 18. 18 THE END….