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Working with Deep Reinforcement Learning

Course Summary

The Working with Deep Reinforcement Learning training course will cover the main ideas of deep reinforcement learning and some of the main tools and frameworks as well as leveraging widely-used Python-based libraries students may have encountered in machine learning spaces. Conventional Machine Learning works best when it is possible to find stable, representative labeled data from which it can find connections between the input features and the predictive outcomes. The effort to produce this labeled data is not always feasible or cost-effective.

The course begins with reinforcement learning systems that mimic the successes of established learning approaches found in the fields of neuroscience and animal conditioning research. Next, the remarkably successful techniques of Deep Learning systems used in dynamic environments evolve to include the ability of "learning to learn". The course concludes with generalizing much wider problem spaces including sophisticated gameplay, online ad-placement, digital resource management, optimized control systems, and self-driving vehicles.

In addition to covering the main ideas of deep reinforcement learning, we will cover some of the main tools and frameworks as well as leveraging widely-used Python-based libraries students have probably already run into in machine learning spaces.

Prerquisites:

  • Previous experience with Machine Learning:
  • Python
Purpose
Learn about deep reinforcement learning, what it is, how it works, and how you can apply it to real-world problems.
Audience
Data Engineers, Data Scientists, and Software Engineers who need to work on learning systems that are more dynamic and sophisticated than simply learning from static data.
Role
Data Engineer | Data Scientist | Software Developer
Skill Level
Intermediate
Style
Lecture | Hands-on Activities | Labs
Duration
2 Days
Related Technologies
Deep Learning | Reinforcement Learning | Python | Machine Learning

 

Course Objectives
  • Define the concepts of agents, environments, states, actions, and rewards.
  • Describe and use the major learning approaches of the fast-changing world of Deep reinforcement learning systems.
  • Identify possible applications of deep reinforcement learning in your organization or industry.

What You'll Learn:

In the Working with Deep Reinforcement Learning training course, you'll learn:
  • Intro to Deep Learning
    • What is deep learning and why do we need it?
      • Differences between Machine Learning (ML) and Deep Learning (DL)
      • Key concepts: Neurons, layers, activation functions
      • Use cases and impact across industries (e.g., healthcare, finance, robotics)
      • Limitations and challenges in deep learning
    • Building Simple Neural Networks
      • Overview of neural network structure
      • Understanding input, hidden, and output layers
      • Activation functions (ReLU, sigmoid, tanh, etc.) and their roles
      • Step-by-step example of a single-layer neural network
    • Network Architecture followed by some exercise
      • Types of neural networks: Fully connected, Convolutional, Recurrent
      • Introduction to key architecture choices: Number of layers, neurons, and connectivity
      • Practical exercise: Building a basic feedforward network with a chosen dataset
      • Discussing the impact of architecture on performance and training time
  • Training & Regularization
    • Activations
      • Role of activation functions in neural networks
      • Common activation functions and when to use them
      • Effect on gradient flow and vanishing/exploding gradient problems
    • Loss functions
      • Types of loss functions for different tasks (classification, regression)
      • Derivation and intuition of commonly used loss functions: Cross-entropy, Mean Squared Error
      • Selecting appropriate loss functions for specific applications
    • Optimizers
      • Gradient descent and its variants (SGD, RMSprop, Adam)
      • Comparison of optimizer efficiency, speed, and accuracy
      • Practical examples and visualizations of optimization paths
    • Regularization
      • Types of regularization: L1, L2, Dropout, Batch Normalization
      • The importance of regularization in reducing overfitting
      • Practical implementation of dropout and batch normalization
  • Data Preprocessing
    • Collecting Data
      • Sources of data for deep learning projects
      • Ethical considerations and bias in data collection
      • Data augmentation strategies to expand training datasets
    • Preprocessing Structured Data followed by Practice Exercise
      • Data cleaning: Handling missing values, dealing with outliers
      • Scaling and normalization techniques for structured data
      • Encoding categorical variables
      • Preparing a structured dataset for neural network input
    • Image Data Preprocessing
      • Image scaling, normalization, and augmentation
      • Converting images into tensors for neural network input
      • Common image preprocessing libraries (OpenCV, PIL)
      • Image dataset preparation and basic transformations
  • Reinforcement Learning (RL)
    • Intro to RL
      • Differences between supervised, unsupervised, and reinforcement learning
      • Key RL concepts: Agents, environments, actions, rewards
      • Real-world applications of RL (e.g., robotics, gaming, autonomous vehicles)
    • Q-learning, Frozen Lake Game
      • Basics of Q-learning and the Q-table
      • Explanation of the Frozen Lake game as an RL environment
      • Practical implementation of Q-learning with Frozen Lake
    • Discretization and Reward Augmentation
      • Discretization techniques for continuous environments
      • The importance of reward shaping and augmentation
      • Examples of reward functions in different RL scenarios
  • Deep Reinforcement Learning (DRL)
    • Intro to Deep Q-Learning
      • Differences between Q-learning and Deep Q-learning (DQN)
      • Explanation of the DQN architecture and improvements over basic Q-learning
      • Introduction to experience replay and target networks
    • Lunar Landing with Deep Q-Learning
      • Overview of the Lunar Lander environment in OpenAI Gym
      • Setting up DQN to solve Lunar Lander
      • Implementing experience replay and target network updates
      • Performance evaluation and parameter tuning
    • Breakout with Deep Q-Learning
      • Introduction to the Atari Breakout game as an RL environment
      • Developing a DQN agent for the Breakout environment
      • Advanced techniques: Double DQN, Prioritized Experience Replay
      • Evaluating the agent’s performance and improvement strategies
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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