An interesting benefit of deep learning neural networks is that they can be reused on related problems. Transfer learning refers to a technique for predictive modeling on a different but somehow similar problem that can then be reused partly or wholly to accelerate the training and improve the performance of a model on the problem of interest. In deep learning, this means reusing the weights in one or more layers from a pre-trained network model in a new model and either keeping the weights fixed, fine tuning them, or adapting the weights entirely when training the model. In this tutorial, you will discover how to use transfer learning to improve the performance deep learning neural networks in Python with Keras. How to Improve Performance With Transfer Learning for Deep Learning Neural Networks Photo by Damian Gadal, some rights reserved. Transfer learning generally refers to a process where a model trained on one problem is used in some way on a second related problem.

Accenture Joins Forces with MIT Professional Education to Reinvent Their Quality Engineering Workforce Quality engineers are being trained to be catalysts for speed, agility and improved business performance NEW YORK; Jan. 29, 2019 – Accenture (NYSE: ACN) is collaborating with MIT Professional Education to launch a new training program aimed at training the company's quality engineers pivot from being software testers to catalysts for speed, agility and business performance. The program, 'Reinventing Quality Engineers in the New,' will train Accenture employees on real-time, insight-driven quality engineering approaches, augmented by artificial intelligence, analytics and autonomous frameworks--a vision outlined in a recent Accenture whitepaper. The jointly developed program provides Accenture engineers with opportunities to grow their skill set in ways that enhance both their own careers and Accenture's work with clients. Employees will learn how to effectively apply analytics and intelligent, model-based automation to software testing and engineering services, as well as advanced risk-based testing approaches that optimize cost and quality levels. The program consists of live virtual classroom sessions that include engagement with MIT professors, as well as self-study materials and opportunities to collaborate outside of the classroom through interactive online forums.

A step-by-step tutorial on how to adapt and finetune BERT for a Kaggle Challenge classification task: The Kaggle Toxic Comment Classification Challenge. This post covers pretty much everything from data processing to model modifications with code examples for each part. Results are in the top-10% of this $35.000

This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.

Are you looking guidelines for best unity tutorials for beginners, training, and courses? Here is the right place you come for best solution to reduce your problem. You can improve your precise skills through best unity courses. In coming up with the games and various interactive 3D content Unity is a kind of tool considered as an ultimate one for it helps. In this present world game development sector is very demanding. The gaming industry is now ranking the pick and who want to part in this surely grow in future. How to get started with game development, a lot of thinking running through your mind. People increase their interest in this site day by day. They want to build up their Career in this industry. From beginners to professionals all are want to improve their skills because they want to play a vital role in this competitive industry. If you are really passionate about making games so Unity is the best game engine to learn. This system designed to help create video games. Unity is a great feature in this modern era. The ultimate list of best unity tutorial courses will supervise you everything that make your path easier. This course is all about game development and design. If you are passionate and willing to learn code so this course is suggested for you. So you don't need any previous experience to do that course. Learn C# is a powerful modern language for that you need Mac or PC capable of running unity 2018. Also you need a free download of unity 5 to review the original content of the course. This online game development course is the first 7 2D unity games for web,Mac and PC.

The course rating is 4.5.

Do you want to learn Python Programming Language? Learn it from the Best Python Tutorial for Beginners, Certification, Course, and Training that you will find online. Python is a high level, general-purpose programming language. It is widely used by programmers all over the world. This object-oriented programming language has a large and comprehensive standard library. Python was first built in the 1980s and since then it has been developing. The latest version of this programming language, Python 3.0, was released in 2008. Ever since it was built, Python has been used by data scientists and programmers in every country. The best thing about Python is that it is easy to understand and adaptable with any of the operating systems. Anyone can learn Python programming language and use it to analyze data, create applications, develop web, and for many other things. It is the most in-demand programming language of this time. Python programmers get highly paid jobs for their skills. We have found the best courses you can find online to learn Python and listed those in here. These online courses will help you to shape your knowledge of Python. So, get through the list and details about those courses and chose one for yourself. Pierian Data International by Jose Portilla is presenting this online course on Python. You can go from the basics to creating your own applications and games with this course. It has a rating of 4.5 out of 5 on Udemy and over 457,000 enrolled students. This python tutorial for beginners provides 24 hours on-demand video, 19 articles and 19 coding exercises with lifetime access. This course will teach you both Python 2 and Python 3. You will learn to use Jupyter Notebook system and Object-Oriented Programming with online classes. This online course on Python programming language has over 100 lectures. It also includes quizzes, tests and homework assignments. They have 3 major projects to complete a Python portfolio.

In the first part of this series, we introduced the BigML Node-RED bindings and showed how to install and use them to create a simple BigML-powered flow in Node-RED. In this second installment, we are going to create a second flow that will give us the opportunity to consider, in greater detail, important concepts such as input-output matching and node reification. As a second example of how you can use BigML with Node-RED, let's build another flow that will use the ensemble created in our first installment to make a prediction each time a new event comes in. One great way to identify a BigML resource is through a tag you assign it at creation time. The tag could represent what the ensemble is used for, or any other kind of information that can help you distinguish it from other resources of the same type in your BigML account.

Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content. Users interact with content and with each other in a complex and dynamic social environment while simultaneously evolving over time. In order to effectively characterize users and predict their future behavior in such a setting, it is necessary to overcome several challenges. Content heterogeneity and temporal inconsistency of behavior data result in severe sparsity at the user level. In this paper, we propose a novel mutual-enhancement framework to simultaneously partition and learn latent activity profiles of users. We propose a flexible user partitioning approach to effectively discover rare behaviors and tackle user-level sparsity. We extensively evaluate the proposed framework on massive datasets from real-world platforms including Q&A networks and interactive online courses (MOOCs). Our results indicate significant gains over state-of-the-art behavior models ( 15% avg ) in a varied range of tasks and our gains are further magnified for users with limited interaction data. The proposed algorithms are amenable to parallelization, scale linearly in the size of datasets, and provide flexibility to model diverse facets of user behavior.

Artificial Intelligence is now a part of our normal lives. We are surrounded by this technology from automatic parking systems, smart sensors for taking spectacular photos, and personal assistance. Similarly, Artificial Intelligence in education is being felt, and the traditional methods are changing drastically. The academic world is becoming more convenient and personalized thanks to the numerous applications of AI for education. This has changed the way people learn since educational materials are becoming accessible to all through smart devices and computers.