Education



[Online] AI/Machine Learning for beginners

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This is a 1-week/10 hours long, part-time and instructor-led training offered in evening time (New York Timezone) by 6FS.io, a San Francisco based technology company. This training program is built based on 6FS team's years of experience in building large-scale solutions using various various Big Data and AI/ML technologies. This is not a book-based training, rather a hands-on, interactive experience app building apps using AI/ML, delivered by experienced startup CTOs. While learning basic concepts like Python, Jupyter notebooks, and training models and human powered labeling, you'll also learn practical problems and solutions, based on how Dean and Adrian built technology stacks in their previous startups. Let's build a project to gather data from human labeling service like AWS Sage maker GroundTruth.


MAGICS Lab University of San Francisco

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San Francisco is known as a hub of tech innovation, making USF an ideal place to study computer and data science. The location gives students the opportunity to connect professionally with companies everyone knows: Google, Twitter, Facebook – the list goes on. But what opportunities does USF offer students to participate in peer reviewed scholarship, a place where current students and faculty can connect over tech R&D on campus? As of Fall 2018, the answer comes in the form of the weekly MAGICS Lab meetings, a way to gain valuable mentorship and learn about emerging technologies, a place where undergraduate, graduate students, and faculty all have the opportunity to learn, research, and publish together. This group welcomes all skill-levels, from novice to seasoned researchers alike.


On EducationThe Complete Python 3 Course: Beginner to Advanced - CouponED

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Link: The Complete Python 3 Course: Beginner to Advanced his course is designed to fully immerse you in the Python language, so it is great for both beginners and veteran programmers! This diploma in C and Python programming course is a great way to get started in programming. It covers the study of the C and Python group of languages used to build most of the world's object oriented systems. The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering.


Comprehensive TensorFlow.js Example

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First I will walk you through the app functionality and then will dive into implementation details. This app implements a business report execution time prediction use case (this time in JavaScript), which was explained in my previous post -- Report Time Execution Prediction with Keras and TensorFlow. For the model training, I'm using 50 epochs (data is processed in batches of 10) and the learning rate is set to 0.001. Neural Network is based on two processing layers and one output layer. Model is trained to forecast the expected wait time for business report execution.


Digitization! The Next Step In Reforming The Education Industry

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Education is the premise of progress, in every society, in every family"- Kofi Annan Education is the backbone of every society and education must be made available to everyone. To ensure this we have to modify the traditional method of learning and adopt new technologies in this field. Digitization is turning as an aid in accomplishing the same, it had made learning electronic and widen its availability to 24 7. Anybody with internet access can learn anything at any time, the boundaries to education have been destructed. Technologies in the education sector are changing the methodology for both educators and modern-day learners. People are adopting new methods other than just bookish learning and prefer a more realistic learning experience. Digitization has brought drastic changes in the education sector nationally and globally. Let us see what various personalities have viewpoints on the same. "Yes to some extent digitization can help today's industrial age education industry in reaching the unreachable population of the country, that said, reform is only possible when the education system is rebooted to focus on the needs of the 21st century and beyond.


Activation maps for deep learning models in a few lines of code

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Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another -- image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation -- just to name a few. However, they are like anything but classical Machine Learning (ML) algorithms/techniques. DL models use millions of parameters and create extremely complex and highly nonlinear internal representations of the images or datasets that are fed to these models. They are, therefore, often called the perfect black-box ML techniques. We can get highly accurate predictions from them after we train them with large datasets, but we have little hope of understanding the internal features and representations of the data that a model uses to classify a particular image into a category.


Inspur Open-Sources TF2, a Full-Stack FPGA-Based Deep Learning Inference Engine

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Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at https://github.com/TF2-Engine/TF2. Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.


How to Deploy Machine Learning Models on Mobile and Embedded Devices

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Thanks to libraries such as Pandas, scikit-learn, and Matplotlib, it is relatively easy to start exploring datasets and make some first predictions using simple Machine Learning (ML) algorithms in Python. Although, to make these trained models useful in the real world, it is necessary to make them available to make predictions on either the Web or Portable devices. In two of my previous articles, I explained how to create and deploy a simple Machine Learning model using Heroku/Flask and Tensorflow.js. Today, I will instead explain to you how to deploy Machine Learning models on Smartphones and Embedded Devices using TensorFlow Lite. TensorFlow Lite is a platform developed by Google to train Machine Learning models on mobile, IoT (Interned of Things) and embedded devices.


A Deeper Understanding Is Needed To Improve Neural Networks

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The development of neural networks is not a new thing. In fact, neural networks have been around since the 1940s, according to MIT News. No one has really been interested in the application of this technology until now. To begin, let's define a neural network. According to the definition by Investopedia: "A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so, the network generates the best possible result without needing to redesign the output criteria."