Course Overview:
The Deep Learning with Python course provides a comprehensive introduction to deep reinforcement learning, neural networks, and AI-driven decision-making. This 3-day program covers key techniques, including policy iteration, reward shaping, and deep Q-learning. Participants will explore how artificial agents learn through experience, applying Python-based deep learning principles to develop intelligent models.
Through hands-on exercises, learners will build and optimize AI agents while gaining expertise in frameworks that support Deep Learning with Python. The course delves into advanced topics such as BURLAP, partially observable Markov decision processes (MDPs), and optimization algorithms.
By the end of the course, attendees will be equipped with practical skills to implement reinforcement learning models in real-world applications. Whether you are a network engineer, automation specialist, or AI enthusiast, this course offers the essential knowledge needed to excel in artificial intelligence and Deep Learning with Python.
Introduction:
Deep Learning refers to the ability of an “artificial agent” to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human’s ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.
Artificial intelligence is transforming industries, and reinforcement learning is a key driver of AI advancements. The Deep Learning with Python course introduces participants to AI systems that learn from experience rather than relying solely on traditional machine learning approaches.
This training covers essential techniques such as neural networks, policy iteration, and deep Q-learning to develop intelligent models. With hands-on exercises, learners will gain practical experience in reinforcement learning and Python-based AI applications.
By completing this course, participants will be well-prepared to build and optimize AI models for various real-world scenarios, strengthening their expertise in machine learning and Deep Learning with Python.
Course Objectives:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Pre-requisites:
- Proficiency in Python
- An understanding of college Calculus and Linear Algebra
- Basic understanding of Probability and Statistics
- Experience creating machine learning models in Python and Numpy
Target Audience:
- Network Administrators, Network (Systems) Engineers, Network (Service) Technicians, Network Analysts, Network Managers
- Junior Programmers, Test Engineers, Test Automation Engineers, QA Engineers and Analysts
Course Duration:
- 3 Days (21 Hours)
Course Content:
- Introduction
- Reinforcement Learning Basics
- Basic Reinforcement Learning Techniques
- Introduction to BURLAP
- The convergence of Value and Policy Iteration
- Reward Shaping
- Exploration
- Generalization
- Partially Observable MDPs
- Options
- Logistics
- TD Lambda
- Policy Gradients
- Deep Q-Learning
- Topics in Game Theory
- Q and A
Conclusion:
The Deep Learning with Python course empowers professionals with practical knowledge in deep reinforcement learning and AI-driven solutions. Through hands-on projects, participants will master techniques such as neural networks and deep Q-learning while applying Python-based frameworks to real-world challenges.
Whether you’re an IT professional, automation engineer, or AI enthusiast, this course provides the essential skills to implement reinforcement learning effectively. With industry-relevant training, learners will enhance their AI expertise and gain confidence in developing scalable AI models. Enroll in this course today to advance your artificial intelligence knowledge and leverage the power of Deep Learning with Python.
