Udemy – PyTorch for Deep Learning with Python Bootcamp 2019-9 – Download

Description

For in-depth learning of PyTorch with Python Bootcamp, Udemy’s PyTorch library video tutorial. PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is quickly becoming the most popular deep learning framework for Python. Deep integration in Python allows popular libraries and packages to be used to easily write neural network layers in Python.

This course focuses on balancing important theory concepts with hands-on exercises and projects that teach you how to incorporate course concepts into your dataset.

PyTorch course feature for deep learning with Python Bootcamp

Learn the classic principles of machine learning theory
Develop deep learning models for working with tabular data
Learn how to use NumPy to format array data
Learn how to use the pandas library to process and delete data
Using PyTorch with a Recursive Neural Network for Time Series Data
Tutorial Using the PyTorch Deep Learning Library for Image Classification

Course Specifications

English
Duration: 17:00:56
Number of lessons: 97
Instructor: Jose Portilla
File format: mp4

Course titles

Course overview, installations and setup
2 lectures 25:02

COURSE OVERVIEW APPROVAL CHECK
0 lectures 00:00

Crash course: NumPy
7 lectures 46:23

Crash Course: Pandas
9 lectures 01:13:19

PyTorch Basics
7 lectures 54:32

An overview of machine learning concepts
6 lectures 46:57

ANN – Artificial Neural Networks
22 lectures 04:44:09

CNN – Convolutional Neural Networks
22 lectures 04:21:05

Recurrent Neural Networks
12 lectures 02:10:20

Using the GPU with PyTorch and CUDA
2 lectures 30:47

NLP with PyTorch
7 lectures 01:08:14

BONUS SECTION: THANK YOU!
1 lecture 00:11

Course prerequisites

An understanding of basic Python topics (data types, loops, functions) is also recommended by Python OOP
Can work through basic derivative calculations
Administrator rights on your computer (ability to download our files)