WHAT YOU’LL LEARN in PyTorch for Deep Learning
- 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
