#cplusplus#arm#baidu#deep_learning#embedded#fpga#mali#mdl#mobile#mobile_deep_learning#neural_network
Paddle Lite is a lightweight, high-performance deep learning inference framework designed to run AI models efficiently on mobile, embedded, and edge devices. It supports multiple platforms like Android, iOS, Linux, Windows, and macOS, and languages including C++, Java, and Python. You can easily convert models from other frameworks to PaddlePaddle format, optimize them for faster and smaller deployment, and run them with ready-made examples. This helps you deploy AI applications quickly on various devices with low memory use and fast speed, making it ideal for real-time, resource-limited environments. It also supports many hardware accelerators for better performance.
https://github.com/PaddlePaddle/Paddle-Lite
Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras
Time series prediction problems are a difficult type of predictive modeling problem.
Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables.
A powerful type of #neural_network designed to handle #sequence dependence is called #recurrent_neural_networks . The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in #deep_learning because very large architectures can be successfully trained.
https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
https://kivy.org/#home
Kivy - Open source Python library for rapid development of applications
that make use of innovative user interfaces, such as #multi_touch_apps.
Developing #mobile applications using Python
http://www.aparat.com/v/0scM5
Irene Chen A Beginner's Guide to Deep Learning.
What is #Deep_Learning ? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to #machine_learning.
http://www.aparat.com/v/Corus
Advanced users #Deep_Learning, anyone who has followed #machine_learning over the past years has heard it. In this talk I will go past the hype and show what deep learning actually means and how one goes about solving complex machine learning task with a minimum amount of code, with the help of theano, an amazing python library for deep learning.
https://www.analyticsvidhya.com/blog/2016/08/deep-learning-path/?utm_content=bufferd56c5&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer
#Deep_Learning, a prominent topic in #Artificial_Intelligence domain, has been in the spotlight for quite some time now. It is especially known for its breakthroughs in fields like Computer Vision and Game playing (Alpha GO), surpassing human ability. Since the last survey, there has been a drastic increase in the trends. (click here to check out the survey)
Here is what Google trends shows us:
https://medium.com/@hassanabid/creating-react-native-apps-with-django-rest-api-59e8417865e9
Creating React Native apps with Django rest-api
I will really appreciate if you support the project by clicking star on Github repository. I will publish new version soon! https://github.com/hassanabidpk/react_pyconlunch
Last week, I delivered a talk about Django for #mobile applications at Pycon Korea. Over the past 6 years, I have been mostly developing mobile applications and contributing to company’s SDKs. Things have changed over past couple of years, as I am no more depending on backend developers to spin off a server for me. Neither I am interested to use automated services like Parse (RIP) or Firebase which hides the complexity and elegance of a backend for mobile applications. I decided to use Django as backend for my mobile applications. Its flexible, stable and customizable. In this blog post, I am going to share basic steps for developing a #React Native app with #Django_rest_api.
https://github.com/BVLC/caffe
#Caffe is a #deep_learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/learning-path-data-science-python/
Comprehensive learning path – #Data_Science in Python
Journey from a Python noob to a Kaggler on Python
So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for #data_analysis. If you already have some background, or don’t need all the components, feel free to adapt your own paths and let us know how you made changes in the path.
You can also check the mini version of this learning path
#Deep_Learning