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Machine Learning vs. Deep Learning – An Example Implementation

While Machine Learning (ML) and Deep Learning are part of the AI family, this webinar delves into Deep Learning and its different capabilities.

Machine Learning vs. Deep Learning A Deep Learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, Deep Learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than that of standard Machine Learning models.

Deep learning is applied to fields such as:

  • computer vision
  • speech recognition
  • natural language processing
  • audio recognition
  • social network filtering
  • machine translation
  • bioinformatics
  • drug design

The results produced using Deep Learning are comparable to – and in sometimes superior to – human experts.  Deep Learning is what powers the most human-like artificial intelligence.

With frameworks like Caffe, TensorFlow, Theano, Keras, etc., choosing the right platform and the design to build your Deep Learning architecture is important. But first, knowing how Deep Neural Networks work can potentially unleash the true power behind this melange of science and technology.


Webinar Presenters

Vinayak-Joglekar-Co-Founder-and-CTO-SynerzipKrishnakumar-BhavsarJoin Vinayak Jogelekar, Synerzip’s Co-Founder and CTO, along with Krishna Bhavsar, Machine Learning Architect and Author of Natural Language Processing with Python Cookbook, in our interactive webinar where they discuss how Deep Learning works compared to Machine Learning.  They will share a real-world case study that demonstrates the power of Deep Learning to improve precision and recall.