Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!

What you'll 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

Requirements
Understanding of Python Basic Topics (data types,loops,functions) also Python OOP recommended
Be able to work through basic derivative calculations
Admin Permissions on your computer (ability to download our files)
Description
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!
-Jose
Who this course is for:
Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch

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