In the previous tutorial, we covered how to use channels to send and receive values with goroutines. That said, it was just a basic example. In reality, we're likely to have questions of synchronization and iterating through known, or unknown, numbers of channel returns.

Text tutorials and sample code: https://pythonprogramming.net/go/
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex

Time series forecasting has a wide range of applications: finance, retail, healthcare, IoT, etc. Recently deep learning models such as ESRNN or N-BEATS have proven to have state-of-the-art performance in these tasks. Nixtlats is a python library that we have developed to facilitate the use of these state-of-the-art models to data scientists and developers, so that they can use them in productive environments. Written in pytorch, its design is focused on usability and reproducibility of experiments. For this purpose, nixtlats has several modules:

Data: contains datasets of various time series competencies.
Models: includes state-of-the-art models.
Evaluation: has various loss functions and evaluation metrics.

Objective:

- To introduce attendees to the challenges of time series forecasting with deep learning.
- Commercial applications of time series forecasting.
- Describe nixtlats, their components and best practices for training and deploying state-of-the-art models in production.
- Reproduction of state-of-the-art results using nixtlats from the winning model of the M4 time series competition (ESRNN).

Project repository: https://github.com/Nixtla/nixtlats.

Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/data...
Instagram: https://www.instagram.com/databricksinc/

Courtesy: Warner Bros. / HBO Max | "Justice League" (2017), "Zack Snyder's Justice League" (2021) and "Zack Snyder's Justice League: Justice Is Gray" (2021) are available for streaming on HBO Max now.

#RestoreTheSnyderVerse, please.

Feel free to Like, Share & Subscribe.
or hit that dislike - Twice.

#ZackSnydersJusticeLeague​ #JusticeIsGray​

An in-depth guide to tensorboard with examples in plotting loss functions, accuracy, hyperparameter search, image visualization, weight visualization as well as tensorboards visual embedding or projector.

The functions we look at from Pytorchs tensorboard are add_scalar, add_image, add_histogram, add_embedding and add_hparams.

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

❤️ Become a Channel Member:
https://www.youtube.com/channe....l/UCkzW5JSFwvKRjXABI

✅ One-Time Donations:
Paypal: https://bit.ly/3buoRYH
Ethereum: 0xc84008f43d2E0bC01d925CC35915CdE92c2e99dc

▶️ You Can Connect with me on:
Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
GitHub - https://github.com/aladdinpersson

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf


OUTLINE:
0:00 - Introduction
2:08 - Initializing SummaryWriter
2:55 - Creating a loss and accuracy plot
5:40 - Doing Hyperparameter search
16:36 - Visualizing Dataset Images and Network Weights
20:55 - Tensorboard Embedding Projector