PyTorch Deep Learning in 7 Days

PyTorch Deep Learning in 7 Days
PyTorch Deep Learning in 7 Days
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 09m | 1.22 GB

Seven short lessons and a daily exercise, carefully chosen to get you started with PyTorch Deep Learning faster than other courses

PyTorch is Facebook’s latest Python-based framework for Deep Learning. It has the ability to create dynamic Neural Networks on CPUs and GPUs, both with a significantly less code compared to other competing frameworks. PyTorch has a unique interface that makes it as easy to learn as NumPy.

This 7-day course is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advance concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks.

By the end of the course, you will be able to build Deep Learning applications with PyTorch.

This hands-on course will get you up-and-running with PyTorch in a week. It is composed of seven lessons. Each video covers one single concept or a set of code modules explained via step-by-step code walkthrough. The complete lesson is systematically explained and is followed by an assignment to spend time on your own as an exercise.

What You Will Learn

  • Get comfortable with most commonly used PyTorch concepts, modules and API including Tensor operations, data representations, and manipulation
  • Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers
  • Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing
  • Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems
  • Implement state of the art in Natural Language Processing to solve real-world problems such as sentiment analysis
  • Implement a simple Generative Adversarial Network to generate fancy images after training on a large image dataset