If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
For the past month, we ranked nearly 250 Machine Learning Open Source Projects to pick the Top 10. We compared projects with new or major release during this period. Mybridge AI ranks projects based on a variety of factors to measure its quality for professionals. Open source projects can be useful for programmers. Hope you find an interesting project that inspires you.
The advent of automated machine learning platforms has expanded the access and availability of algorithmic interpretation over the past several years. But how do the different machine learning platforms stack up from a performance perspective? That's the question that researchers from Arizona State University sought to answer. As the market for machine learning platforms expands, users are naturally inclined to seek sources of information to rank and rate the various options that are available to them. Which systems are the easiest to use?
Machine learning in essence, is the research and application of algorithms that help us better understand data. By leveraging statistical learning techniques from the realm of machine learning, practitioners are able to draw meaningful inferences from and turn data into actionable intelligence. Furthermore, the availability of several open source machine learning tools, platforms and libraries today enables absolutely anyone to break into this field, utilizing a plethora of powerful algorithms to discover exploitable patterns in data and predict future outcomes. This development in particular has given rise to a new wave of DIY retail traders, creating sophisticated trading strategies that compete (and in some cases, outperform others) in a space previously dominated by just institutional participants. In this introductory blog post, we will discuss supportive reasoning for, and different categories of machine learning.
In this blog, I will show you how to implement a trading strategy using the regime predictions made in the previous blog. Do read it, there is a special discount for you at the end of this. There is one thing that you should keep in mind before you read this blog though: The algorithm is just for demonstration and should not be used for real trading without proper optimization. First, I imported the necessary libraries. If you do not have this package, I suggest you install it first or change your data source to google.
This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. See the LICENSE file for license rights and limitations (MIT).
"Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions." I like to think of machine learning as computer programs that produce different results as they are exposed to more information without changing their source code (and consequently needed to be redeployed). For example, consider a game that I play with the computer. I show the computer this picture and tell it "Blue Circle".
Based on the summary, it's evident that Python is a better choice over other languages mainly because it being generic enough that it not just good for statistical/machine-learning related tasks but other generic tasks and having better support for all DNN frameworks like Tensorflow, Caffe. But, R can be very handy for a quick prototype that doesn't need to use DNN frameworks. So, to summarize, R is the choice of language for a quick prototype but for long term python is the choice of language.
On my last tutorial exploring OpenCV, we learned AUTOMATIC VISION OBJECT TRACKING. This project was done with this fantastic "Open Source Computer Vision Library", the OpenCV. On this tutorial, we will be focusing on Raspberry Pi (so, Raspbian as OS) and Python, but I also tested the code on My Mac and it also works fine. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. I am using a Raspberry Pi V3 updated to the last version of Raspbian (Stretch), so the best way to have OpenCV installed, is to follow the excellent tutorial developed by Adrian Rosebrock: Raspbian Stretch: Install OpenCV 3 Python on your Raspberry Pi.
Transcribing audio files or speech is vital for many companies around the world and as we know, the old school technique of Listen -- Transcribe by humans may cause fatal errors and eats up lot of your resources(humans). It requires painstaking attention to transcribe every word that's being recorded and sometimes, you have to deal with multiple audio files. 'What a drag', is exactly what Shikamaru would say if he was given the job of transcribing and here's where Google Speech API and it's latest addition, Time offsets (timestamps) comes to the rescue, for us Shikamarus. What is Google Speech API?
On today's episode of "The Interview" with The Next Platform, we discuss the role of higher level interfaces to common machine learning and deep learning frameworks, including Caffe. Despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs) according to this episode's guest, Soren Klemm, one of the creators of Python based Barista, which is an open-source graphical high-level interface for the Caffe framework. While Caffe is one of the most popular frameworks for training DNNs, editing prototxt files in order to specify the net architecture and hyper parameters can become a cumbersome and error-prone task. Instead, Barista offers a fully graphical user interface with a graph-based net topology editor. Barista is designed on top of the Caffe infrastructure.