Deep Learning with PyTorch

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!

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

01:49:15

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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Deep Learning with PyTorch

About Course

What Will You Learn?

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

PyTorch Images and Logistic Regression

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

Training Deep Neural Networks on a GPU

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

Image Classification with Convolutional Neural Networks

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Shopping cart

About Course

What Will You Learn?

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

PyTorch Images and Logistic Regression

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

Training Deep Neural Networks on a GPU

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

Image Classification with Convolutional Neural Networks

Student Ratings & Reviews

Compare items

Shopping cart

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 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