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) …
First we'll need to write a function that can take an unclassified entry and perform a prediction on it. To do this, the script will need to rebuild the model in memory based on the pickle file (model.dat, in this case), and feed it a new entry to allow it to make a prediction. While it's possible to retrain a model from scratch each time we want to make a prediction, this is incredibly resource intensive (especially in larger examples) and is a fundamentally different process from making a standalone inference, and as such, is very bad practice in machine learning. I've written a predict function within a new file, predict.py(below), For this prediction, the model requires 4 numerical inputs (sepal_length, sepal_width, petal_length, petal_width -- in this order) and returns a class prediction containing one of three species (Iris-setosa, Iris-versicolor, Iris-virginica).
Text summarization is the task of creating short, accurate, and fluent summaries from larger text documents. Recently deep learning methods have proven effective at the abstractive approach to text summarization. In this post, you will discover three different models that build on top of the effective Encoder-Decoder architecture developed for sequence-to-sequence prediction in machine translation. Encoder-Decoder Deep Learning Models for Text Summarization Photos by Hiếu Bùi, some rights reserved. Take my free 7-day email crash course now (with sample code).
Let us understand this with a simple analogy. Driving a car has always excited me, since my childhood I've imagined of owning a car and steering endlessly on the highways. To fulfill my dream, as soon as I attained the age of 18 I enrolled myself into a driving school. So excited to hold the steering in my hand! I was careful to note all the instructions conveyed by the instructor.
Machine learning continues to gain headway, with more organizations and industries adopting the technology to do things like optimize operations, improve inventory forecasting and anticipate customer demand. Recent research from the McKinsey Global Institute found that total annual external investment in AI was between $8 billion and $12 billion in 2016, with machine learning attracting nearly 60 percent of that investment. What's more, organizations with senior management support for machine learning and AI initiatives reportedly stand to increase profit margins anywhere from 3 percent to 15 percent. Despite this momentum, many organizations struggle with simple machine learning best practices and miss out on the benefits as a result. Following are 10 tips for organizations who want to use machine learning more effectively.
Cloud services are designed to take away a lot of the complexity associated with managing a particular process, whether that's software or infrastructure. Today, machine learning is quickly gaining traction with developers, and AWS wants to help remove some of the obstacles associated with building and deploying machine learning models. To that end, the company announced Amazon SageMaker, a new service that provides a framework for developers and data scientists to manage the machine learning model process while removing some of the heavy lifting that is typically involved. Randall Hunt wrote in a blog post announcing the new service that the idea is provide a framework for accelerating the process of getting machine learning incorporated in new applications. "Amazon SageMaker is a fully managed end-to-end machine learning service that enables data scientists, developers, and machine learning experts to quickly build, train and host machine learning models at scale," Hunt wrote.
In this post, I'm going to cover tricks and best practices for how to write the most effective reward functions for reinforcement learning models. If you're unfamiliar with deep reinforcement learning, you can learn more about it here before jumping into the post below. Crafting reward functions for reinforcement learning models is not easy. It's not easy for the same reason that crafting incentive plans for employees is not easy. We get things affectionately known as the cobra effect.
Automatic photo captioning is a problem where a model must generate a human-readable textual description given a photograph. It is a challenging problem in artificial intelligence that requires both image understanding from the field of computer vision as well as language generation from the field of natural language processing. It is now possible to develop your own image caption models using deep learning and freely available datasets of photos and their descriptions. In this tutorial, you will discover how to prepare photos and textual descriptions ready for developing a deep learning automatic photo caption generation model. How to Prepare a Photo Caption Dataset for Training a Deep Learning Model Photo by beverlyislike, some rights reserved.
Algorithmia started out as an online marketplace for -- can you guess it? Many of these algorithms that developers offered on the service focused on machine learning (think face detection, sentiment analysis, etc.). Today, with the boom in ML/AI, that's obviously a big draw and Algorithmia is now taking its next step in this direction with the launch of a new service that helps data scientists manage and deploy their machine learning models -- and share them with others inside their companies. This basically means that the company is turning some of the infrastructure and services it built to run these models itself into a new product. "Tensorflow is open-source, but scaling it is not," said Kenny Daniel, co-founder and CTO of Algorithmia, in today's announcement.
For a recent hackathon that we did at STATWORX, some of our team members scraped minutely S&P 500 data from the Google Finance API. The data consisted of index as well as stock prices of the S&P's 500 constituents. Having this data at hand, the idea of developing a deep learning model for predicting the S&P 500 index based on the 500 constituents prices one minute ago came immediately on my mind. Playing around with the data and building the deep learning model with TensorFlow was fun and so I decided to write my first Medium.com What you will read is not an in-depth tutorial, but more a high-level introduction to the important building blocks and concepts of TensorFlow models.