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Deep Reinforcement Learning with Python

21 Hours
Deep Reinforcement Learning mengacu pada kemampuan "agen buatan" untuk belajar dengan trial-and-error dan hadiah-dan-hukuman. Agen buatan bertujuan untuk meniru kemampuan manusia untuk mendapatkan dan membangun pengetahuannya sendiri, langsung dari masukan mentah seperti penglihatan. Untuk mewujudkan pembelajaran penguatan, pembelajaran mendalam dan jaringan saraf digunakan. Pembelajaran penguatan berbeda dari pembelajaran mesin dan tidak bergantung pada pendekatan pembelajaran yang diawasi dan tidak diawasi.Pelatihan langsung yang dipandu instruktur ini (online atau onsite) ditujukan untuk pengembang dan ilmuwan data yang ingin mempelajari dasar-dasar Deep Reinforcement Learning saat mereka melangkah melalui pembuatan Agen Deep Learning.Pada akhir pelatihan ini, peserta akan dapat:
    Memahami konsep kunci di balik Deep Reinforcement Learning dan dapat membedakannya dari Machine Learning. Terapkan algoritme Reinforcement Learning lanjutan untuk memecahkan masalah dunia nyata. Bangun Agen Deep Learning.
Format Kursus
    Kuliah dan diskusi interaktif. Banyak latihan dan latihan. Implementasi langsung di lingkungan lab langsung.
Opsi Penyesuaian Kursus
    Untuk meminta pelatihan khusus untuk kursus ini, silakan hubungi kami untuk mengaturnya.
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Fundamentals of Reinforcement Learning

21 Hours
Reinforcement Learning (RL) is a machine learning technique in which a computer program (agent) learns to behave in an environment by performing the actions and receiving feedback on the results of the actions. For each good action, the agent receives positive feedback, and for each bad action, the agent receives negative feedback (penalty). This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets. By the end of this training, participants will be able to:
  • Install and apply the libraries and programming language needed to implement Reinforcement Learning.
  • Create a software agent that is capable of learning through feedback instead of through supervised learning.
  • Program an agent to solve problems where decision making is sequential and finite.
  • Apply knowledge to design software that can learn in a way similar to how humans learn.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Reinforcement Learning with Java

21 Hours
Reinforcement Learning (RL) is an area of AI (Artificial Intelligence) used to build autonomous systems (e.e., an "agent") that learn by interacting with their environment in order to solve a problem. RL has applications in areas such as robotics, gaming, consumer modeling, healthcare, supply chain management, and more. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization. By the end of this training, participants will be able to:
  • Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
  • Analyze a real-world problem and redefine it as Reinforcement Learning problem.
  • Implementing a solution to a real-world problem using Reinforcement Learning.
  • Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Testimonials(10)

The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide. It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.

Terry Strachan - Exel Computer Systems plc
Course - OptaPlanner in Practice

examples and exercises

Kamil
Course - Introduction to Data Science and AI using Python

The course is very interesting being the main focus nowdays

mohamed taher - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course

Mohamed Ghowaiba - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

Helpful and good listener .. interactive

Ahmed El Kholy - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

All the information presented

Jose Victor - si
Course - Artificial Intelligence (AI) for Managers

The exercises and examples presented.

Marcos - si
Course - Artificial Intelligence (AI) for Managers

Machine Learning Themes

Víctor Edgar - si
Course - Artificial Intelligence (AI) for Managers

The teacher's attitude

Ivonne Guadalupe Avendaño Hernandez - si
Course - Artificial Intelligence (AI) for Managers

The concepts taught were clear, practical and help a lot in getting an idea of how to use this AI & ML topic.

Miguel - si
Course - Artificial Intelligence (AI) for Managers

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