Building Blocks for Deep Learning in the Wolfram Language

Mixkit: Known for its user-friendly interface, Mixkit provides a vast collection of free video assets, including motion graphics and video templates. While they aren't directly from Videohive, they offer a comparable quality that can be integrated into your projects seamlessly.
Videezy: Videezy offers a variety of free and paid video assets, including After Effects templates. Their extensive library is perfect for those looking to enhance their videos without breaking the bank.
Motion Elements: While Motion Elements primarily focuses on premium content, they regularly feature free downloads that include video templates similar to those found on Videohive. Their search interface allows you to filter out the free options easily.
Editor’s Depot: This site provides free editing tools and templates, ensuring you have access to some elements similar to Videohive offerings. Though the selection might not be as vast, it’s a valuable resource for cost-effective production.
Motion Array: With a solid reputation, Motion Array occasionally offers free downloads that replicate some Videohive elements. Subscribing to their newsletter might help you get alerts about any new freebies available.

Categories: Tutorials » IT and Software

https://www.linkedin.com/learning/building-blocks-for-deep-learning-in-the-wolfram-language

This course explores how to construct neural networks in the Wolfram Language. The Wolfram Language neural network framework provides symbolic building blocks to build, train, and tune a network, as well as automatically process input and output using encoders and decoders. Discover how to build feed-forward networks, and learn about recurrent neural nets and why they are interesting.