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Advanced AI: Deep Reinforcement Learning in Python

6/3/2019

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What you will learn in this course?

  • Build various deep learning agents
  • Application of various advanced reinforcement learning algorithms to any problem
  • Q-Learning with Deep Neural Networks
  • Policy Gradient Methods with Neural Networks
  • Reinforcement Learning with RBF Networks
  • Use Convolutional Neural Networks with Deep Q-Learning
  • TD Lambda algorithm
  • A special type of neural network (i.e. the RBF network)
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About this course

In this course, you’ll work with more complex environments, specifically provided by the OpenAI Gym: CartPole Mountain Car Atari games to train effective learning agents so you’ll need new techniques. It’s all about the application of neural networks to reinforcement learning and deep learning.

We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.Supervised and unsupervised machine learning algorithms are for making predictions about data and analyzing, while reinforcement learning is about training an agent to interact with an environment and maximize its reward. Deep reinforcement learning and AI has a lot of potentials also carries huge risk.

One main principle of training reinforcement learning agents is that there are unintended consequences when training an AI. AI’s don’t think like humans, they come up with novel and non-intuitive solutions to reach their goals; often in ways that surprise domain experts.One of the great things about OpenAI is that they have a platform called the OpenAI Gym (heavy use of in this course). In order to train their reinforcement learning agents in the standard environments, it allows everyone and everywhere in this world.

Requirements-Basics of reinforcement learning, Dynamic Programming, TD Learning,Monte Carlo,MDPs; Knowledge of Calculus and probability(Undergraduate level); Experience in building models in machine learningPython and Numpy; Know how to build a feedforward, recurrent neural network using Theano and Tensorflow; convolutional.

Click the link or image below to access the course contents:
​​Advanced AI: Deep Reinforcement Learning in Python

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Learn Python AI for Image Recognition & Fraud Detection

4/30/2019

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What you will learn in this course?

  • Learn the actual coding behind popular sites such as YouTube and Instagram that is in Python, a popular coding language used for most of the websites present nowadays.
  • Learn TensorFlow, an open source library for numerical computation that makes machine learning easier and faster.
  • Learn how to build models of linear regression.
  • Make an image recognition model with CIFAR.
  • Make a Credit or debit Card Fraud Detection Model using python language. Learn how to keep your data safe.
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About this course

In this course, you will learn basics to high level Python language, its application and calculation of linear regression with TensorFlow, and use Artificial Intelligence and machine learning for automation in different industry.

Together with a professional in Python coding language with more than 10+ years of work and teaching experience you will easily perform CIFAR 10 image data recognition .At the end of this course it will be able to analyze credit and debit card fraud by building projects and models on it.

​This course comes up with study material for beginners to get comfortable and hands on with the interfaces of Python software. You can also find the following related courses in study material:

• Hands-On Image Recognition: Python Data Science Bootcamp!
• Fraud Detection with Python, TensorFlow & Linear Regression!
• Detect Fraud and Predict the Stock Market with TensorFlow


Click the link or image below to access the course contents:
​Learn Python AI for Image Recognition & Fraud Detection

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