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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with basic to intermediate IT and programming skills

Who should attend & recommended skills

  • This course is geared for those who want to get to grips with building powerful deep learning models using PyTorch and scikit-learn.
  • Skill-level: Foundation-level Data Python skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Programming skills: Basic to Intermediate (1-5 years’ experience)

About this course

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.

Skills acquired & topics covered

  • How you can speed up the deep learning process with one-shot learning
  • Using Python and PyTorch to build state-of-the-art one-shot learning models
  • Architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning.
  • Getting to grips with the fundamental concepts of one- and few-shot learning
  • Working with different deep learning architectures for one-shot learning
  • Understanding when to use one-shot and transfer learning, respectively
  • Studying the Bayesian network approach for one-shot learning
  • Implementing one-shot learning approaches based on metrics, models, and optimization in PyTorch
  • Different optimization algorithms that help to improve accuracy even with smaller volumes of data
  • Various one-shot learning architectures based on classification and regression

Course breakdown / modules

  • Technical requirements
  • The human brain – overview
  • Machine learning – historical overview
  • One-shot learning – overview
  • Setting up your environment
  • Coding exercise

  • Technical requirements
  • Parametric methods – an overview
  • Understanding Siamese networks
  • Understanding matching networks
  • Coding exercise

  • Technical requirements
  • Understanding Neural Turing Machines
  • Memory-augmented neural networks
  • Understanding meta networks
  • Coding exercises

  • Technical requirements
  • Overview of gradient descent
  • Understanding model-agnostic meta-learning
  • Understanding LSTM meta-learner

  • Technical requirements
  • Overview of Bayesian learning
  • Understanding directed graphical models
  • Overview of probabilistic methods
  • Bayesian program learning
  • Discriminative k-shot learning

  • Recent advancements
  • Related fields
  • Application