Modern deep learning libraries such as Keras allow you to define and start fitting a wide range of neural network models in minutes with just a few lines of code. Nevertheless, it is still challenging to configure a neural network to get good performance on a new predictive modeling problem. The challenge of getting good performance can be broken down into three main areas: problems with learning, problems with generalization, and problems with predictions. Once you have diagnosed the specific type of problem that you are having with a network, a suite of classical and modern techniques can then be selected to address the issue and improve performance. In this post, you will discover a framework for diagnosing performance problems with deep learning models and techniques that you can use to target and improve each specific performance problem.

In the changing tech scenario in India, noted and well-established institutes have now also started to step forward and train students as well as the professionals in artificial intelligence and machine learning. The institutes are providing both the current needs of algorithms and mathematical insights as well as practical experiences. In this article, we list 5 tier-1 institutes that have added courses on artificial intelligence in India. About The Programme: This institute launched a dual degree specialisation in data science as well as in robotics in the year 2018. Any B.Tech student can enroll in this programme based on the CGPA cut-off of 8.0 at the end of the 5th semester.

This is the bite size course to learn Java Programming for Machine Learning and Statistical Learning with Weka library. In CRISP DM data mining process, machine learning is at the modeling and evaluation stage. You will need to know some Java programming, and you can learn Java programming from my "Create Your Calculator: Learn Java Programming Basics Fast" course. You will learn Java Programming for machine learning and you will be able to train your own prediction models with naive bayes, decision tree, knn, neural network, linear regression, and evaluate your models very soon after learning the course.

Deep learning neural networks learn how to map inputs to outputs from examples in a training dataset. The weights of the model are initialized to small random values and updated via an optimization algorithm in response to estimates of error on the training dataset. Given the use of small weights in the model and the use of error between predictions and expected values, the scale of inputs and outputs used to train the model are an important factor. Unscaled input variables can result in a slow or unstable learning process, whereas unscaled target variables on regression problems can result in exploding gradients causing the learning process to fail. Data preparation involves using techniques such as the normalization and standardization to rescale input and output variables prior to training a neural network model. In this tutorial, you will discover how to improve neural network stability and modeling performance by scaling data. How to Improve Neural Network Stability and Modeling Performance With Data Scaling Photo by Javier Sanchez Portero, some rights reserved.

Python is a general purpose programming language created in 1990 by Guido Van Rossum It was heavily adopted by YouTube and has since powered some of the most impressive websites in the world. In this series we'll take a look at all the common constructs of the Python Programming Language. Python has been one of the fastest growing languages for decades and is now a top 4 programming language in the world. The course will explain the fundamentals of programming, types and object-oriented programming principles. After taking this course, students should be able to branch off into Machine Learning, Web Development, Automation or even Gaming.

Before we cover some Machine Learning finance applications, let's first understand what Machine Learning is. Machine Learning (ML) is a part of data science that uses different models to analyze data and make predictions. The cool thing about machine learning is that, just like how babies learn to walk and speak through experience, Machine Learning Software also learns how to analyze data from experience. You don't need to explicitly teach it anything. You simply give it a set of data for which results are already known and let it process it to identify patterns in the data and corresponding results.

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.