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Deep Neural Network (DL)

Artificial intelligence is growing exponentially. There is no doubt about that. Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge.

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DEEP LEARNING (Deep Neural Network)

Artificial intelligence is growing exponentially. There is no doubt about that. Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.

But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

  • Unsupervised Learning
  • Reinforcement Learning
  • Artificial Neural Networks to solve a Customer Churn problem
  • Convolutional Neural Networks for Image Recognition
  • Recurrent Neural Networks to predict Stock Prices
  • Self-Organizing Maps to investigate Fraud
  • Boltzmann Machines to create a Recommender System
  • Understand the intuition behind Artificial Neural Networks
  • Apply Artificial Neural Networks in practice
  • Understand the intuition behind Convolutional Neural Networks
  • Apply Convolutional Neural Networks in practice
  • Understand the intuition behind Recurrent Neural Networks
  • Apply Recurrent Neural Networks in practice
  • Understand the intuition behind Self-Organizing Maps
  • Apply Self-Organizing Maps in practice
  • Understand the intuition behind Boltzmann Machines
  • Apply Boltzmann Machines in practice
  • Understand the intuition behind AutoEncoders
  • Apply AutoEncoders in practice
  • Supervised Learning
  • Tensorflow and Pytorch

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