Deep Learning Course — PyTorch Deep Learning Python Bootcamp

Deep Learning Course, Learn how to create advanced neural networks for deep learning with the PyTorch Deep Learning library!

What You Will Learn in This Deep Learning Course

• Learn how to use NumPy to format data into arrays

• Use Pandas for data manipulation and cleaning

• Learn the principles of classical machine learning theory

• Use PyTorch Deep Learning Library for image classification

• Use PyTorch with Recurrent Neural Networks for Time Series Sequence Data

• Build advanced Deep Learning models to work with tabular data

Requirements

• Understanding of Basic Python Topics (data types, loops, functions) and Python OOP is also recommended

• Ability to work through basic derivative calculations

• Admin permission on your computer (ability to download our files)

Deep Learning Course Description

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

This course focuses on balancing important theoretical concepts with hands-on practical exercises and projects that allow you to learn how to apply the concepts in the course to your own datasets! When you enroll in this course, you will gain access to carefully crafted notebooks that explain concepts in an easy-to-understand way, including code and explanations side by side. You will also have 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 Split

• Model Evaluation – Regression and Classification Tasks

• Unsupervised Learning Tasks

• Tensors with PyTorch

• Artificial Neural Network Perceptrons Theory