/explore
|
You are here 
|
bryanbrattlof.com | ||
| | | | |
www.jeremymorgan.com
Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits. |
|
| | | | | ||
| | | | |
jaketae.github.io
Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is... |
|
| | | | | ||
| | | | |
kavita-ganesan.com
This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which tasks they are designed to learn. |
|
| | | | | ||
| | | | |
cpury.github.io
Learning how to produce Bible-like texts with Recurrent Neural Networks |
|
| | | |||
