PyTorch-Deep Learning and Artificial Intelligence [updated]

PyTorch: Deep Learning and Artificial Intelligence



What you'll learn

Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
Predict Stock Returns
Time Series Forecasting
Computer Vision
How to build a Deep Reinforcement Learning Stock Trading Bot
GANs (Generative Adversarial Networks)
Recommender Systems
Image Recognition
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Natural Language Processing (NLP) with Deep Learning
Demonstrate Moore's Law using Code
Transfer Learning to create state-of-the-art image classifiers

Requirements

Know how to code in Python and Numpy
For the theoretical parts (optional), understand derivatives and probability

Description

Welcome to PyTorch: Deep Learning and Artificial Intelligence!



Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.

Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?

Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?

It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)

On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.

If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.

udp://tracker.leechers-paradise.org:6969/announce

udp://tracker.coppersurfer.tk:6969/announce

udp://tracker.opentrackr.org:1337/announce

udp://tracker.zer0day.to:1337/announce

udp://eddie4.nl:6969/announce

udp://tracker.internetwarriors.net:1337/announce

udp://zephir.monocul.us:6969/announce

udp://wassermann.online:6969/announce

udp://vibe.community:6969/announce

udp://valakas.rollo.dnsabr.com:2710/announce

udp://us-tracker.publictracker.xyz:6969/announce

udp://tracker0.ufibox.com:6969/announce

udp://tracker.zerobytes.xyz:1337/announce

udp://tracker.v6speed.org:6969/announce

udp://tracker.uw0.xyz:6969/announce