Deep Learning
- 1. TECHNICAL SEMINAR ON DEEP LEARNING PRESENTED BY MANJUNATHA SAI UPPU 16KA1A0512 Department of Computer Science and Engineering
- 2. CONTENTS • Introduction. • Timeline. • Why deep learning • Fields in D. L. • Frameworks of D. L. • difference between A. I and D. L. • Applications of deep learning. • Relation to human cognitive and brain development. • Commercial activity. • Startups using deep learning. • References
- 3. INTRODUCTION • Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. • Capable of studying and analyzing data representations of images, sounds, video and other data, deep learning aims to replicate the activity that occurs in our grey matter, recognizing patterns and making sophisticated deductions.
- 4. HISTORY • It trace backs to 1934 when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the human brain. They used a combination of algorithms and mathematics they called “threshold logic” to mimic the thought process
- 6. FIELDS IN DEEP LEARNING • Neural networks • Computer vision • Natural language processing • Sentiment analysis • Detection of required analysis. Ex cancer cell detection, lane detection • Generative adversarial networks • Recurrent neural networks • Convolutional neural networks
- 8. DIFFERENCE BETWEEN A. I. AND D. L.
- 12. RELATION TO HUMAN COGNITIVE AND BRAIN DEVELOPMENT • Deep learning is closely related to a class of theories of brain development (specifically, neocortical development) proposed by cognitive neuroscientists. • A 1995 description stated, "...the infant's brain seems to organize itself under the influence of waves of so-called trophic-factors ... different regions of the brain become connected sequentially, with one layer of tissue maturing before another and so on until the whole brain is mature.“ • A variety of approaches have been used to investigate the plausibility of deep learning models from a neurobiological perspective. Other researchers have argued that unsupervised forms of deep learning, such as those based on hierarchical generative models and deep belief networks, may be closer to biological reality.
- 13. RELATION TO HUMAN COGNITIVE AND BRAIN DEVELOPMENT • Although a systematic comparison between the human brain organization and the neuronal encoding in deep networks has not yet been established, several analogies have been reported. • The computations performed by deep learning units could be similar to those of actual neurons and neural populations. Similarly, the representations developed by deep learning models are similar to those measured in the primate visual system both at the single-unit and at the population levels.
- 14. COMMERCIAL ACTIVITY • Facebook's AI lab performs tasks such as automatically tagging uploaded pictures with the names of the people in them. • Google's DeepMind Technologies developed a system capable of learning how to play Atari video games using only pixels as data input. In 2015 they demonstrated their AlphaGo system, which learned the game of Go well enough to beat a professional Go player. • Google Translate uses an LSTM to translate between more than 100 languages. • In 2015, Blippar demonstrated a mobile augmented reality application that uses deep learning to recognize objects in real time.
- 15. COMMERCIAL ACTIVITY • In 2017, Covariant.ai was launched, which focuses on integrating deep learning into factories. • As of 2008,researchers at The University of Texas at Austin (UT) developed a machine learning framework called Training an Agent Manually via Evaluative Reinforcement, or TAMER, which proposed new methods for robots or computer programs to learn how to perform tasks by interacting with a human instructor. • Deep TAMER used deep learning to provide a robot the ability to learn new tasks through observation. Using Deep TAMER, a robot learned a task with a human trainer, watching video streams or observing a human perform a task in-person. The robot later practiced the task with the help of some coaching from the trainer, who provided feedback such as “good job” and “bad job.”
- 17. REFERNECES: • https://en.wikipedia.org/wiki/Deep_learning • http://deeplearning.net/tutorial/ • https://developers.google.com/machine-learning/crash-course/ • https://pathmind.com/wiki/neural-network
- 18. THANK YOU