Everything from getting started with using PyTorch to building your own real-world models
Why PyTorch is a fantastic way to start working in machine learning
Understand how to integrate Deep Learning into tools and applications
Create and utilize machine learning algorithms just like you would write a Python program
Build and deploy your own custom trained PyTorch neural network accessible to the public
How to take data, build a ML algorithm to find patterns, and then use that algorithm as an AI to enhance your applications
Master deep learning and become a top candidate for recruiters seeking Deep Learning Engineers
To expand your Machine Learning and Deep Learning skills and toolkit
The skills you need to become a Deep Learning Engineer and get hired with a chance of making US$100,000+ / year
Are there any prerequisites for this course?
Required: A computer (Linux/Windows/Mac) with an internet connection Basic Python knowledge
Recommended: Previous Machine Learning knowledge is recommended, but not required. Daniel provides sufficient supplementary resources to get you up-to-speed .Experience using Jupyter Notebooks or Google Colab is recommended
Who is this course for?
Anyone who wants a step-by-step guide to learning PyTorch and be able to get hired as a Deep Learning Engineer making over $100,000/year
Students, developers, and data scientists who want to demonstrate practical machine learning skills by actually building and training real models using PyTorch
Anyone looking to expand their knowledge and toolkit when it comes to AlI, Machine Learning and Deep Learning
Bootcamp or online PyTorch tutorial graduates that want to go beyond the basics
Students who are frustrated with their current progress with all of the beginner PyTorch tutorials out there that don’t go beyond the basics and don’t give you real-world practice or skills you need to actually get hired
