Size: 2.28 GB
The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks
What Will I Learn?
 Build various deep learning agents
 Apply a variety of advanced reinforcement learning algorithms to any problem
 QLearning with Deep Neural Networks
 Policy Gradient Methods with Neural Networks
 Reinforcement Learning with RBF Networks
 Use Convolutional Neural Networks with Deep QLearning
Requirements
 Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning
 Calculus and probability at the undergraduate level
 Experience building machine learning models in Python and Numpy
 Know how to build a feedforward, convolutional, and recurrent neural network using Theano and Tensorflow
Description
This course is all about the application of deep learning and neural networks to reinforcement learning. CartPole
 Mountain Car
 Atari games
 Calculus
 Probability
 Objectoriented programming
 Python coding: if/else, loops, lists, dicts, sets
 Numpy coding: matrix and vector operations
 Linear regression
 Gradient descent
 Know how to build a feedforward, convolutional, and recurrent neural network in Theano and TensorFlow
 Markov Decision Proccesses (MDPs)
 Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve MDPs
 Watch it at 2x.
 Take handwritten notes. This will drastically increase your ability to retain the information.
 Write down the equations. If you don’t, I guarantee it will just look like gibberish.
 Ask lots of questions on the discussion board. The more the better!
 Realize that most exercises will take you days or weeks to complete.
 Write code yourself, don’t just sit there and look at my code.
 (The Numpy Stack in Python)
 Linear Regression in Python
 Logistic Regression in Python
 (Supervised Machine Learning in Python)
 (Bayesian Machine Learning in Python: A/B Testing)
 Deep Learning in Python
 Practical Deep Learning in Theano and TensorFlow
 (Supervised Machine Learning in Python 2: Ensemble Methods)
 Convolutional Neural Networks in Python
 (Easy NLP)
 (Cluster Analysis and Unsupervised Machine Learning)
 Unsupervised Deep Learning
 (Hidden Markov Models)
 Recurrent Neural Networks in Python
 Artificial Intelligence: Reinforcement Learning in Python
 Natural Language Processing with Deep Learning in Python
 Advanced AI: Deep Reinforcement Learning in Python
Who is the target audience?
 Professionals and students with strong technical backgrounds who wish to learn stateoftheart AI techniques.
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