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Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    DLWPYTL21E09

Who should attend & recommended skills:

Those with Python experience seeking better understanding of deep learning, computer vision, NLP, & generative models

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others, with basic IT and Linux skills who want to build understand through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable.
  • This course also explores challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
  • Skill-level: Foundation-level Deep Learning with Python skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Python: Intermediate (3-5 years’ experience) required
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Keras, TensorFlow, or Machine Learning: Not required
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them”
  • “This course is geared for Python experienced developers, analysts or others who build and train the latest and greatest deep learning models and contribute to making a dent in the world and who wish to learn how to implement deep learning algorithms with Python and PyTorch.
  • Skill-level: Foundation-level Deep Learning with PyTorch skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)
  • Linear Algebra: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-lineoptions such as ls, cd, cp, and su
  • Deep Learning: Basic (1-2 years’ experience) helpful
  • PyTorch or other deep learning frameworks: Not required

About this course

Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This course takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you’ll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you’ll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You’ll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences. After covering the basics, the course will take you on a journey through larger projects. The centerpiece of the course is a neural network designed for cancer detection. You’ll discover ways for training networks with limited inputs and start processing data to get some results. You’ll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you’ll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning!

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Deep Learning with Python expert instructor, students will learn about and explore:
  • Challenging concepts and practice with applications in computer vision, natural-language processing, and generative models
  • By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
  • Deep learning from first principles
  • Image-classification, imagine segmentation, and object detection
  • Deep learning for natural language processing
  • Timeseries forecasting
  • Neural style transfer, text generation, and image generation

Course breakdown / modules

  • Artificial intelligence, machine learning, and deep learning
  • Before deep learning: a brief history of machine learning
  • Why deep learning? Why now?

  • A first look at a neural network
  • Data representations for neural networks
  • The gears of neural networks: tensor operations
  • The engine of neural networks: gradient-based optimization
  • Looking back at our first example

  • What TensorFlow?
  • What Keras?
  • Keras and TensorFlow: a brief history
  • Setting up a deep-learning workspace
  • First steps with TensorFlow
  • Anatomy of a neural network: understanding core Keras APIs

  • Classifying movie reviews: a binary classification example
  • Classifying newswires: a multiclass classification example
  • Predicting house prices: a regression example

  • Generalization: the goal of machine learning
  • Evaluating machine-learning models
  • Improving model fit
  • Improving generalization

  • Define the task
  • Success
  • Develop a model
  • Deploy your model