After a few months of covering the "Bleeding Edge" (whether the news from NeuIPS or the new features in TensorFlow), we are returning to 'regular Deep Learning' this month. In particular, we'll have speakers covering the use of CNNs for images, from the ground up.
Planned Talks :
"First steps in Deep Learning with TensorFlow 2.0 : CNNs" - Martin Andrews
This talk aims to cover the "something for beginners" part of our tagline - motivating the building blocks of CNNs, how they are trained, and how the resulting model can be applied to different datasets. Code examples will be provided in Colab notebooks.
"PreTrained CNNs and tf.keras.applications" - Timothy Liu
In this talk, Timothy will discuss (and demonstrate) the advantages of using pre-trained CNNs for vision tasks. An undergraduate at SUTD, Timothy has interned at Nvidia and is actively involved in setting up SUTD's GPU resources.
"Tips for Image Classification in TensorFlow 2.0" - Sam Witteveen
While knowing how to train a CNN is great, it doesn't guarantee that you will train a good model that generalizes well in a reasonably quick amount of time. In this talk, Sam will show some slightly more advanced techniques that beginners can use to improve their models. These include how to use TensorFlow's new Datasets API to get data to train on and how to do such tasks as Image Augmentation in training, Test Time Augmentation and Progressive Resizing of images.
Talks will start at 7:00pm and end at around 9:00pm, at which point people normally come up to the front for a bit of a chat with each other, and the speakers.
As always, we're actively looking for more speakers for future events - both '30 minutes long-form', and lightning talks. For the lightning talks, we welcome folks to come and talk about something cool they've done with TensorFlow and/or Deep Learning for 5-10mins (so, if you have slides, then #max=10). We believe that the key ingredient for the success of a Lightning Talk is simply the cool/interesting factor. It doesn't matter whether you're an expert or and enthusiastic beginner: Given the responses we have had, we're sure there are lots of people who would be interested to hear what you've been playing with. Please suggest yourself here :