Deep Learning on DataThrillz

Recurrent Neural Networks in PyTorch

Mon, 07 Sep 2020 17:13:17 +0000

Feed forward networks cannot learn from the past, but Recurrent Neural Networks (RNNs) can learn by accepting data in a sequence. Examples of applications for RNNs include the text autocomplete feature on your phone and performing language translations.

Recurrent Neurons (RNs) act as the building blocks of RNNs. The difference between RNs and feed forward neurons is that RNs accept input x, at time t, as well as a hidden state or output from time t-1 from another RN. The output of a RN is a vector, unlike for a feed forward neuron. RNNs are trained (i.e. their weights are calculated) using backpropagation via Gradient Descent Optimization in time. Output from a single RNN layer at time instance,t is an input to the next layer. Each layer in the RNN represents an instance in time.

Fastai’s Practical Deep Learning for Coders Course Release - 2020 Update!

Thu, 03 Sep 2020 17:48:01 +0000

On Aug 21st, 2020, fastai released a new version of their Practical Deep Learning for Coders -Part 1 course. This course is a must-take for new and intermediate deep learning practitioners. It is well done and teaches you intuition without drowning you in theory. The only prerequisites are some high-school math, and a year of coding experience (preferably in Python). This course is free and can be done without any installation, by taking advantage of the Colab and/or Gradient platforms, which provide free, GPU-powered Python notebooks. Go here to learn more about the course.

Regression & Classification Models in PyTorch

Tue, 05 May 2020 23:41:00 +0000

The purpose of this article is to share what I learned from a recent PyTorch course. We will share general machine learning tips as well as insights specific to deep learning library PyTorch.

PyTorch is a deep learning library for Python and was created by Facebook in 2016. PyTorch is good for deep learning beginners. There are several other popular deep learning frameworks such as TensorFlow, Keras, Chainer, and ONNX. TensorFlow was developed by Google and now includes Keras (previously a separate framework). I chose to deepen my knowledge in PyTorch because it is easy to learn and is commonly used for deep learning. Fastai is another deep learning open source library that is really awesome and intuitive to use. While using Fastai, I found myself curious about the inner workings so it’s a plus that fastai wraps PyTorch as I can gain a better understanding of both libraries at once. Two birds, one stone!