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Deep Learning with PyTorch

Categories: IT & Computer Science
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About Course

Welcome to the best online course for learning about Deep Learning with Python and PyTorch!

PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.

This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations.

In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including:

  • NumPy
  • Pandas
  • Machine Learning Theory
  • Test/Train/Validation Data Splits
  • Model Evaluation – Regression and Classification Tasks
  • Unsupervised Learning Tasks
  • Tensors with PyTorch
  • Neural Network Theory
    • Perceptrons
    • Networks
    • Activation Functions
    • Cost/Loss Functions
    • Backpropagation
    • Gradients
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • and much more!

By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.

So what are you waiting for? Enroll today and experience the true capabilities of Deep Learning with PyTorch! I’ll see you inside the course!

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What Will You Learn?

  • Learn how to use NumPy to format data into arrays
  • Use pandas for data manipulation and cleaning
  • Learn classic machine learning theory principals
  • Use PyTorch Deep Learning Library for image classification
  • Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data
  • Create state of the art Deep Learning models to work with tabular data

Course Content

PyTorch Basics and Gradient Descent
Introduction to machine learning and Jupyter notebooks PyTorch basics: tensors, gradients, and autograd Linear regression & gradient descent from scratch Using PyTorch modules: nn.Linear & nn.functional

  • PyTorch Basics and Gradient Descent

PyTorch Images and Logistic Regression
Working with images from the MNIST dataset Training and validation dataset creation Softmax function and categorical cross entropy loss Model training, evaluation, and sample predictions

Training Deep Neural Networks on a GPU
Working with cloud GPU platforms like Kaggle & Colab Creating a multilayer neural network using nn.Module Activation function, non-linearity, and universal approximation theorem Moving datasets and models to the GPU for faster training

Image Classification with Convolutional Neural Networks
Working with the 3-channel RGB images from the CIFAR10 dataset Introduction to Convolutions, kernels & features maps Underfitting, overfitting, and techniques to improve model performance

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