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PyTorch Ultimate: From Basics to Cutting-Edge

 

PyTorch Ultimate: From Basics to Cutting-Edge

Very good course. It is compact and to the point giving you practical "templates" on how to apply different classes of DL algorithms in PyTorch.

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What you'll learn in PyTorch Ultimate: From Basics to Cutting-Edge

  • understand Deep Learning fundamentals
  • learn all relevant aspects of PyTorch to develop state-of-the-art models
  • develop image classification models
  • develop object detection models
  • create artistic images with Style Transfer
  • use transfer learning
  • adapt top-notch algorithms like Transformers to custom datasets

Welcome On Udemy Course PyTorch Ultimate: From Basics to Cutting-Edge

PyTorch is a Python framework developed by Facebook to develop and deploy Deep Learning models. It is one of the most popular Deep Learning frameworks nowadays.

You will learn everything that is needed for developing and applying deep learning models to your own data. All relevant and state of the art model architectures will be covered. After this course you will be able to apply your knowledge to your own data so that you can train models for regression, classification, computer vision, and time series data.

It is important to me that you learn the underlying concepts as well as how to implement the techniques. You will be challenged to tackle problems on your own, before I present you my solution.

In my course I will teach you:

  • Introduction to Deep Learning
  • high level understanding
  • perceptrons
  • layers
  • activation functions
  • loss functions
  • optimizers
  • Tensor handling
  • creation and specific features of tensors
  • automatic gradient calculation (autograd)
  • Modeling introduction, incl.
  • Linear Regression from scratch
  • understanding PyTorch model training
  • Batches
  • Datasets and Dataloaders
  • Hyperparameter Tuning
  • saving and loading models
  • Classification models
  • multilabel classification
  • multiclass classification
  • Convolutional Neural Networks
  • CNN theory
  • develop an image classification model
  • layer dimension calculation
  • image transformations
  • Object Detection
  • object detection theory
  • develop an object detection model
  • Style Transfer
  • Style transfer theory
  • developing your own style transfer model
  • Pretrained Models and Transfer Learning
  • Recurrent Neural Networks
  • Recurrent Neural Network theory
  • developing LSTM models
  • Autoencoders
  • Transformers
  • Understand Transformers, including Vision Transformers (ViT)
  • adapt ViT to a custom dataset
  • Generative Adversarial Networks

Enroll right now to learn some of the coolest techniques and boost your career with your new skills.

Best regards,

Bert