Machine Learning Strategies for Time Series Forecasting

Machine Learning Strategies for Time Series Forecasting

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Forecasting time-series data has applications in many fields, including finance, health, etc. There are potential pitfalls when applying classic statistical and machine learning methods to time-series problems. This talk will give folks the basic toolbox to analyze time-series data and perform forecasting using statistical and machine learning models, as well as interpret and convey the outputs.

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PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

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Implementing Deep Learning Papers - Deep Deterministic Policy Gradients (using Python)

Implementing Deep Learning Papers - Deep Deterministic Policy Gradients (using Python)

Implementing Deep Learning Papers - Deep Deterministic Policy Gradients (using Python)

In this intermediate deep learning tutorial, you will learn how to go from reading a paper on deep deterministic policy gradients to implementing the concepts in Tensorflow. This process can be applied to any deep learning paper, not just deep reinforcement learning.

In the second part, you will learn how to code a deep deterministic policy gradient (DDPG) agent using Python and PyTorch, to beat the continuous lunar lander environment (a classic machine learning problem).

DDPG combines the best of Deep Q Learning and Actor Critic Methods into an algorithm that can solve environments with continuous action spaces. We will have an actor network that learns the (deterministic) policy, coupled with a critic network to learn the action-value functions. We will make use of a replay buffer to maximize sample efficiency, as well as target networks to assist in algorithm convergence and stability.

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Machine Learning, Data Science and Deep Learning with Python

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