Narendra Gangwani Machine learning, deep learning, RL, python
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I have taught Tabular and Approximate solution methods in Reinforcement Learning to several graduate and undergrad students across the world.

Machine Learning Algorithms, Supervised, Unsupervised, semi supervised, Deep Learning, Neural Networks, CNNs, RNNs, GANs, Reinforcement Learning, Monte Carlo, Q Learning, temporal difference, Tabular methods, Approximate methods, Deep q learning, Actor Critic, TD Lambda, Offline Reinforcement Learning, Natural language Processing, Computer Vision.
Data structures and algorithms, python, tensorflow, pytorch, scikitlearn, keras, opencv, numpy, pandas.

Math courses, Linear Algebra, Calculus, Limits, Derivatives, Integration, Trigonometry, Vector Calculus, Convex Optimization, Real analysis, Functional Analysis, Statistics and Probability, Probability theory, Bayesian Statistics, differential equations, Statistical learning theory.

Subjects

  • Deep Learning Bachelors/Undergraduate-Masters/Postgraduate

  • GRE Quantitative Aptitude

  • Reinforcement Learning Grade 12-Bachelors/Undergraduate

  • Machine learning with python Bachelors/Undergraduate

  • Python 3 Grade 12-Bachelors/Undergraduate

Experience

  • Research Assistant (Aug, 2020–May, 2022) at UTD
    Deep learning
    Reinforcement Learning

Education

  • MsCS (Aug, 2020–May, 2022) from UTD

Fee details

    US$15–30/hour (US$15–30/hour)

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