What is Reinforcement learning?[ML0to100] — S1E6

Original article was published on Artificial Intelligence on Medium

Summary Cheat Sheets, Notes, Flash Cards, Google Colab Notebooks, codes, etc will all be provided in further lessons as required.

Read through the whole ‘S1’ [ML0to100] series to learn about-

  • What Machine Learning is, what problems it tries to solve, and the main categories and fundamental concepts of its systems.
  • The steps in a typical Machine Learning project
  • Learning by fitting a model to data
  • Optimizing a cost function
  • Handling, cleaning and preparing data
  • Selecting and engineering features
  • Selecting a model and tuning hyperparameters using cross-validation
  • The challenges of Machine Learning, in particular, underfitting and overfitting (the bias/variance trade-off)
  • The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods
  • Reducing the dimensionality of the training data to fight the “curse of dimensionality”
  • Other unsupervised learning techniques, including clustering, density estimation, and anomaly detection Part II, Neural Networks and Deep Learning, covers the following topics:
  • What neural nets are and what they’re good for building and training neural nets using TensorFlow and Keras
  • The most important neural net architectures: feedforward neural nets for tabular data, convolutional nets for computer vision, recurrent nets and long short-term memory (LSTM) nets for sequence processing, encoder/decoders, and Transformers for natural language processing, autoencoders and generative adversarial networks (GANs) for generative learning
  • Techniques for training deep neural nets
  • How to build an agent (e.g., a bot in a game) that can learn good strategies through trial and error, using Reinforcement Learning
  • Loading and preprocessing large amounts of data efficiently
  • Training and deploying TensorFlow models at scale

Disclaimer — This series is based on the notes that I created for myself based on various books I’ve read, so some of the text could be an exact quote from some book out there, I’d have mentioned the book but even I don’t know which book a paragraph belongs to as it’s a compilation. It’s best for the reader as they get the best out of the all promising books available in the market for ML compiled in one place.