Revised text of talk I gave for the Harvard Library Leadership in a Digital Age program. The description of this course promises that "you will identify fundamental changes occurring in the field of knowledge management and consider their implications for libraries, information services, and library leadership." I think my session maybe breaks the rules a bit (which is my first leadership tip for you: when it feels like the right thing to do, break the damn rules!). One of the things I think is important for library leaders is that we look at fundamental changes outside of knowledge management and consider their implications for libraries and the work we do. I think looking outside of changes in our own field is essential if we want to be active, effective leaders who don't merely respond to change, but who create and shape the change we believe is needed in libraries and archives.

We are going to make an Image Classifier by Retraining the Final (Bottleneck) Layer of the Inception-v3 model and then Optimize the model for your smart devices. I'm pretty sure you already know this step, since you are learning to run the same model on the smartphones. Also to keep this tutorials strictly focused on the Implementing models on the smartphones, I would recommend this quick tutorial Train Inception with Custom Images on CPU So we'd be on the same page and you can start the things in a new directory with a newly trained Model. It will create a new optimized model file tf_files/optimized_graph.pb To reduce the pre-processing of an app and to decrease the size of the library at the same time, tensorflow only supports the subset of operations that are commonly used during inference. Now, just to make sure that whatever graph file we've just created includes the supported operations as follows… To make sure that your new optimized graph is running and the optimize_for_inference file that removes all nodes that aren't needed for a given set of input and outputs and hasn't altered the output of the network.

Machine learning (aka A.I.) seems bizarre and complicated. It's the tech behind image and speech recognition, recommendation systems, and all kinds of tasks that computers used to be really bad at but are now really good at. It involves teaching a computer to teach itself. And you can learn to do it in well under a year, according to data scientist Bargava. You'll need to put in a solid 10-20 hours a week, but you will learn a lot along the way.

Thanks to advances in AI and machine learning, a slow but steady transformation is coming to education -- under the hood. When you compare the typical 21st century classroom with that of the early 1900s, the differences aren't terribly obvious. Teachers will be standing in front, giving instructions and sharing notes on a modern-day version of the old blackboard -- say, an overhead projector or a shared computer display. Students will be sitting at their desks in the classroom or watching via online video-conferencing software. The technology has changed: A lot of the tools and processes have been digitized, some of it has been automated, and geographical barriers have been removed to some extent -- but the actors and elements have remained much the same.

Linear algebra is a field of mathematics that could be called the mathematics of data. It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started in machine learning. This is misleading advice, as linear algebra makes more sense to a practitioner once they have a context of the applied machine learning process in which to interpret it. In this post, you will discover why machine learning practitioners should study linear algebra to improve their skills and capabilities as practitioners. Before we go through the reasons that you should learn linear algebra, let's start off by taking a small look at the reason why you should not.

Matrices are a foundational element of linear algebra. Matrices are used throughout the field of machine learning in the description of algorithms and processes such as the input data variable (X) when training an algorithm. In this tutorial, you will discover matrices in linear algebra and how to manipulate them in Python. A Gentle Introduction to Matrices for Machine Learning Photo by Maximiliano Kolus, some rights reserved. Take my free 7-day email crash course now (with sample code).

The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of patterns and the categorization of data into specific sets. Predictive modeling and optimization methods allow unknown events to be categorized based on statistics and classifiers input by researchers. The Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering is a critical reference source that provides comprehensive information on the use of optimization techniques and predictive models to solve real-life engineering and science problems. Through discussions on techniques such as robust design optimization, water level prediction, and the prediction of human actions, this publication identifies solutions to developing problems and new solutions for existing problems, making this publication a valuable resource for engineers, researchers, graduate students, and other professionals.

Artificial Intelligence (AI) is already here, it is radically transforming business and pioneering companies are already leveraging AI resources to create profitable business growth. At the same time there is a lot of hype and fear surrounding the subject. AI, once thought of only in the context of robots on production lines, is poised to infiltrate all industries, markets and professions. While much discussion has focused on job displacement and the fear that computers will one day inherit the earth, less has been said about how businesses at the frontier have begun to take advantage of machine learning to grow and transform their strategies, and about how humans and intelligent machines can collaborate to form new kinds of jobs and business growth. 'Making Artificial Intelligence Work for You', a new program from the Rotman School of Management, seeks to demystify AI in a business context and to provide an up-to-the-minute understanding of its potential benefits and risks.

At untapt, all of our models involve Natural Language Processing (NLP) in one way or another. Our algorithms consider the natural, written language of our users' work experience and, based on real-world decisions that hiring managers have made, we can assign a probability that any given job applicant will be invited to interview for a given job opportunity. With the breadth and nuance of natural language that job-seekers provide, these are computationally complex problems. We have found deep learning approaches to be uniquely well-suited to solving them. To share my love of deep learning for NLP, I have created five hours of video tutorial content paired with hands-on Jupyter notebooks.

For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning. There seems, however, to be an increasing trend of getting on to the practical from the start, and mixing practice and theory along the way as resources progress. This post presents 5 such resources. Covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks, these resources cover quite a bit of ground. They are also all free, so get reading, get watching, and get coding.