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One-shot Learning with Python

  • Course Code: Artificial Intelligence - One-shot Learning with Python
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to Get to grips with building powerful deep learning models using PyTorch and scikit-learn

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Data Python skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Get to grips with building powerful deep learning models using PyTorch and scikit-learn 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this course, you’ll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples. 

One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The course begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you’ve got to grips with the core principles, you’ll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you’ll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence. 

By the end of this course, you’ll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models. 

Working in a hands-on learning environment, led by Python expert instructor, students will learn about and explore: 

  • Learn how you can speed up the deep learning process with one-shot learning 
  • Use Python and PyTorch to build state-of-the-art one-shot learning models 
  • Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Get to grips with the fundamental concepts of one- and few-shot learning 
  • Work with different deep learning architectures for one-shot learning 
  • Understand when to use one-shot and transfer learning, respectively 
  • Study the Bayesian network approach for one-shot learning 
  • Implement one-shot learning approaches based on metrics, models, and optimization in PyTorch 
  • Discover different optimization algorithms that help to improve accuracy even with smaller volumes of data 
  • Explore various one-shot learning architectures based on classification and regression 

Audience & Pre-Requisites 

This course is for readers want to Get to grips with building powerful deep learning models using PyTorch and scikit-learn 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. 
  • Good foundational mathematics or logic skills 
  • For readers with existing programming skills. 

Course Agenda / Topics 

  1. Section 1: One-shot Learning Introduction 
  • Section 1: One-shot Learning Introduction 
  1. Introduction to One-shot Learning 
  • Introduction to One-shot Learning 
  • Technical requirements 
  • The human brain – overview 
  • Machine learning – historical overview 
  • One-shot learning – overview 
  • Setting up your environment 
  • Coding exercise 
  1. Section 2: Deep Learning Architectures 
  • Section 2: Deep Learning Architectures 
  1. Metrics-Based Methods 
  • Metrics-Based Methods 
  • Technical requirements 
  • Parametric methods – an overview 
  • Understanding Siamese networks 
  • Understanding matching networks 
  • Coding exercise 
  1. 5Model-Based Methods 
  • Model-Based Methods 
  • Technical requirements 
  • Understanding Neural Turing Machines 
  • Memory-augmented neural networks 
  • Understanding meta networks 
  • Coding exercises 
  1. Optimization-Based Methods 
  • Optimization-Based Methods 
  • Technical requirements 
  • Overview of gradient descent 
  • Understanding model-agnostic meta-learning 
  • Understanding LSTM meta-learner 
  1. Section 3: Other Methods and Conclusion 
  • Section 3: Other Methods and Conclusion 
  1. Generative Modeling-Based Methods 
  • Generative Modeling-Based Methods 
  • Technical requirements 
  • Overview of Bayesian learning 
  • Understanding directed graphical models 
  • Overview of probabilistic methods 
  • Bayesian program learning 
  • Discriminative k-shot learning 
  1. Conclusions and Other Approaches 
  • Conclusions and Other Approaches 
  • Recent advancements 
  • Related fields 
  • Application 
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