Have you started leveraging the power of ML in your apps?
While browsing through GitHub for inspirations, I've bookmarked these awesome 20+ open-source machine learning projects
Bring magic to your apps 🧵
⚡️ TensorFlow
The latest version of TensorFlow supports Keras, which is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
There are also interfaces for Javascript and Swift.
Scikit-learn has simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and Matplotlib.
It’s a popular choice to use alongside TensorFlow because of its simplicity and handy functions. scikit-learn.org
⚡️ MXNet
Besides TensorFlow, Keras, and Scikit-learn, there is also the MXNet deep learning framework from Apache. There is a model zoo you can visit for many models implemented in MXNet. github.com/apache/incubat…
⚡️ PyTorch
PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system. github.com/pytorch/pytorch
⚡️ magenta
Magenta is a research project exploring the role of machine learning in the process of creating art and music github.com/tensorflow/mag…
⚡️ style2paints
This project is aimed to colorize line art.
The AI can paint on a sketch according to a given color style, create its own color style to paint on a sketch or transfer another illustration’s style. github.com/lllyasviel/sty…
⚡️ Image-to-image translation in PyTorch
This project has two components—CycleGAN and pix2pix—which contain PyTorch implementations for both unpaired and paired image-to-image translation github.com/junyanz/pytorc…
⚡️ Deep voice conversion
We have some style transfer tools for images and video, but what about voice? Deep voice conversation is a perfect example of this capability. github.com/andabi/deep-vo…
⚡️ StarGAN in PyTorch
It goes beyond style transfer to convert source images by applying different hairstyles, skin types, ages, gender, and different moods. github.com/yunjey/StarGAN
⚡️ Face detection
This may not sound intriguing because now we can do this easily with the help of Core ML or ML Kit on iOS and Android.
But a deeper look shows how awesome this is. Not only can it detect faces, but also emotions and genders. github.com/oarriaga/face_…
⚡️ Deep universal probabilistic programming
Opportunities range from matching riders to drivers, to suggesting optimal routes, finding sensible pool combinations, and even creating the next generation of intelligent vehicles
⚡️ Detectron
Detectron is Facebook AI Research’s software system that implements state-of-the-art object detection algorithms, including Mask R-CNN.
fastText is a library for efficient learning of word representations and sentence classification. github.com/facebookresear…
⚡️ AirSim
AirSim is a simulator for drones, cars, and more built on Unreal Engine.
It’s open-sourced, cross-platform, and it supports hardware-in-loop with popular flight controllers github.com/Microsoft/AirS…
⚡️ Image restoration
Machine learning can do more than we can imagine. With Deep Image Prior, it’s about fixing images with neural networks—but without learning. github.com/DmitryUlyanov/…
⚡️ Open Pose
Open Pose represents the first real-time, multi-person system to jointly detect human body, hand, facial, and foot key points (in total 135 keypoints) on single images.
Hi iOS and macOS developers, where are you currently hanging out?
Here are the active communities on @SlackHQ I've joined and founded useful 🧵👇
🌍 iOS Developers
The most active community for iOS and Swift developers I've found, with over 30,000 members. You can find many familiar faces here eg. @DonnyWals@aaron_pearce
Originally created by @jsngr and @SiddDevs as place to discover new TestFlight apps, now there's active community here discussing and showing new beta.
What are some top programming books you can really recommend?
In the earliest days of my dev career, I was lucky to be shown these programming books and they have helped me a long way.
Sharing here with hope you can skyrocket your dev career 🧵
📚 Code Complete: A Practical Handbook of Software Construction
Written by Steve McConnell in 1993, this book is considered one of the best practical guides to programming. Learn about optimal coding styles, integration, testing, and craftmanship.
Recently I've started reviewing system design and algorithms. While this is not for interview purposes, I find that this knowledge helps in both daily job and indie projects.
Below are a few resources that I've learned and I highly recommend. Thread 👇
1) System Design
System design is about defining architecture and modules interface to satisfy requirements. Whether you're working in a team or solo, clarifying requirements on what you want to do and what is exactly the problem, is most important.
2) System Design concepts
You don't need a CS degree to get started, but by reviewing system design, you know these concepts, which you need everywhere you go
SwiftUI ViewBuilder is resultBuilder, and from Swift 5.3 you can declare if let, if case, multiple if statements, switch, ... pretty much like a normal block function.
ViewBuilder is recommended to leverage SwiftUI type system to ensure performance. Here are a few examples 👇
Local variables.
Pretty much like a normal function, you can declare local variables and return View at last.
Mix if and switch statements
This is handy when you want to show modal or overlay of different Views
Convert JSON into gorgeous, typesafe code in any language. I use this a lot to quickly generate Swift models from server JSON response. It generates very elegant enum handling and optiona