- 15 Python Snippets to Optimize your Data Science Pipeline - Aug 25, 2021.
Quick Python solutions to help your data science cycle.
Data Science, Optimization, Pipeline, Python
- State of Mathematical Optimization Report, 2021 - May 28, 2021.
Download your copy of Gurobi's first-ever "State of Mathematical Optimization Report," which is based on data from a survey of commercial mathematical optimization users. Get yours now.
Gurobi, Optimization, Report
- A Simple Way to Time Code in Python - Mar 18, 2021.
Read on to find out how to use a decorator to time your functions.
Optimization, Programming, Python
- Speeding up Scikit-Learn Model Training - Mar 5, 2021.
If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time.
Distributed Computing, Machine Learning, Optimization, scikit-learn
- Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret - Mar 5, 2021.
PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I'd like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method.
Bayesian, Hyperparameter, Machine Learning, Optimization, PyCaret, Python, scikit-learn
- How to Speed up Scikit-Learn Model Training - Feb 11, 2021.
Scikit-Learn is an easy to use a Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. The question becomes, how do you create the best scikit-learn model in the least amount of time?
Distributed Systems, Hyperparameter, Machine Learning, Optimization, Parallelism, Python, scikit-learn, Training
Optimization Algorithms in Neural Networks - Dec 18, 2020.
This article presents an overview of some of the most used optimizers while training a neural network.
Gradient Descent, Neural Networks, Optimization
- Algorithms for Advanced Hyper-Parameter Optimization/Tuning - Nov 17, 2020.
In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.
Automated Machine Learning, AutoML, Hyperparameter, Optimization, Python
- Strategies of Docker Images Optimization - Oct 8, 2020.
Large Docker images lengthen the time it takes to build and share images between clusters and cloud providers. When creating applications, it’s therefore worth optimizing Docker Images and Dockerfiles to help teams share smaller images, improve performance, and debug problems.
Docker, Optimization, Sciforce, Strategy
- Making Python Programs Blazingly Fast - Sep 25, 2020.
Let’s look at the performance of our Python programs and see how to make them up to 30% faster!
Development, Optimization, Programming, Python
- Showcasing the Benefits of Software Optimizations for AI Workloads on Intel® Xeon® Scalable Platforms - Sep 1, 2020.
The focus of this blog is to bring to light that continued software optimizations can boost performance not only for the latest platforms, but also for the current install base from prior generations. This means customers can continue to extract value from their current platform investments.
AI, Intel, Optimization, Scalability
- Rapid Python Model Deployment with FICO Xpress Insight - Aug 20, 2020.
The biggest hurdle in the use of data to create business value, is indeed the ability to operationalize analytics throughout the organization. Xpress Insight is geared to reduce the burden on IT and address their critical requirements while empowering business users to take ownership of decisions and change management.
AI, Deployment, FICO, Machine Learning, Optimization, Python
- Autotuning for Multi-Objective Optimization on LinkedIn’s Feed Ranking - Aug 19, 2020.
In this post, the authors share their experience coming up with an automated system to tune one of the main parameters in their machine learning model that recommends content on LinkedIn’s Feed, which is just one piece of the community-focused architecture.
Automated Machine Learning, AutoML, LinkedIn, Machine Learning, Optimization
- Facebook Uses Bayesian Optimization to Conduct Better Experiments in Machine Learning Models - Aug 10, 2020.
A research from Facebook proposes a Beyasian optimization method to run A/B tests in machine learning models.
Bayesian, Facebook, Machine Learning, Modeling, Optimization
- Linear algebra and optimization and machine learning: A textbook - May 18, 2020.
This book teaches linear algebra and optimization as the primary topics of interest, and solutions to machine learning problems as applications of these methods. Therefore, the book also provides significant exposure to machine learning.
Book, Charu Aggarwal, Linear Algebra, Machine Learning, Optimization
- Hyperparameter Optimization for Machine Learning Models - May 7, 2020.
Check out this comprehensive guide to model optimization techniques.
Hyperparameter, Machine Learning, Modeling, Optimization, Python
- Optimize Response Time of your Machine Learning API In Production - May 1, 2020.
This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.
API, Machine Learning, Optimization, Production, Python
How to Do Hyperparameter Tuning on Any Python Script in 3 Easy Steps - Apr 8, 2020.
With your machine learning model in Python just working, it's time to optimize it for performance. Follow this guide to setup automated tuning using any optimization library in three steps.
Hyperparameter, Machine Learning, Optimization, Python
- TensorFlow 2.0 Tutorial: Optimizing Training Time Performance - Mar 5, 2020.
Tricks to improve TensorFlow training time with tf.data pipeline optimizations, mixed precision training and multi-GPU strategies.
Neural Networks, Optimization, Python, TensorFlow, Training
- Practical Hyperparameter Optimization - Feb 13, 2020.
An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.
Automated Machine Learning, AutoML, Deep Learning, Hyperparameter, Machine Learning, Optimization, Python, scikit-learn
How to Optimize Your Jupyter Notebook - Jan 30, 2020.
This article walks through some simple tricks on improving your Jupyter Notebook experience, and covers useful shortcuts, adding themes, automatically generated table of contents, and more.
Jupyter, Optimization, Python
How To “Ultralearn” Data Science: summary, for those in a hurry - Dec 30, 2019.
For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.
Advice, Data Science, Experimentation, Optimization, Ultralearn
- Enabling the Deep Learning Revolution - Dec 5, 2019.
Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.
Deep Learning, Gradient Descent, Neural Networks, Optimization
There is No Free Lunch in Data Science - Sep 12, 2019.
There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.
Algorithms, Data Science, Machine Learning, Optimization
- Lagrange multipliers with visualizations and code - Aug 6, 2019.
In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an optimization problem with constraints.
Analytics, Mathematics, Optimization, Python
- From Data Pre-processing to Optimizing a Regression Model Performance - Jul 19, 2019.
All you need to know about data pre-processing, and how to build and optimize a regression model using Backward Elimination method in Python.
Model Performance, Modeling, Optimization, Regression
- XGBoost and Random Forest® with Bayesian Optimisation - Jul 8, 2019.
This article will explain how to use XGBoost and Random Forest with Bayesian Optimisation, and will discuss the main pros and cons of these methods.
Bayesian, Optimization, Python, random forests algorithm, XGBoost
- Top 8 Data Science Use Cases in Construction - Jul 5, 2019.
This article considers several of the most efficient and productive data science use cases in the construction industry.
Optimization, Predictive Analytics, Product Analytics, Risk Analytics, Use Cases
- Optimization with Python: How to make the most amount of money with the least amount of risk? - Jun 26, 2019.
Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobel-prize winning economic theory for maximizing investment profits while minimizing risk.
Finance, Investment, Optimization, Python, Risk Modeling, Stocks
- 10 Gradient Descent Optimisation Algorithms + Cheat Sheet - Jun 26, 2019.
Gradient descent is an optimization algorithm used for minimizing the cost function in various ML algorithms. Here are some common gradient descent optimisation algorithms used in the popular deep learning frameworks such as TensorFlow and Keras.
Algorithms, Deep Learning, Gradient Descent, Optimization
- How to Automate Hyperparameter Optimization - Jun 12, 2019.
A step-by-step guide into performing a hyperparameter optimization task on a deep learning model by employing Bayesian Optimization that uses the Gaussian Process. We used the gp_minimize package provided by the Scikit-Optimize (skopt) library to perform this task.
Bayesian, Deep Learning, Hyperparameter, Machine Learning, Neural Networks, Optimization, Python, TensorFlow
- Linear Programming and Discrete Optimization with Python using PuLP - May 8, 2019.
Knowledge of such optimization techniques is extremely useful for data scientists and machine learning (ML) practitioners as discrete and continuous optimization lie at the heart of modern ML and AI systems as well as data-driven business analytics processes.
Pages: 1 2
Linear Programming, Optimization, Python
- How Optimization Works - Apr 18, 2019.
Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time.
Data Science, Data Scientist, Gradient Descent, Optimization, Prescriptive Analytics
- Checklist for Debugging Neural Networks - Mar 22, 2019.
Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.
Checklist, Modeling, Neural Networks, Optimization, Tips, Training
- Deep Compression: Optimization Techniques for Inference & Efficiency - Mar 20, 2019.
We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.
Compression, Convolutional Neural Networks, Deep Learning, ICLR, Inference, Optimization, Regularization
Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 18, 2019.
This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.
Pages: 1 2
AI, Algorithms, Deep Learning, Machine Learning, Neural Networks, numpy, Optimization, Python
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
Deep Learning, Deep Neural Network, Machine Learning, Neural Networks, Optimization, TensorFlow
- The Intuitions Behind Bayesian Optimization with Gaussian Processes - Oct 19, 2018.
Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.
Bayesian, Distribution, Hyperparameter, Machine Learning, Optimization
Recent Advances for a Better Understanding of Deep Learning - Oct 1, 2018.
A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.
Deep Learning, Explained, Flat Minima, Linear Networks, Machine Learning, Optimization, SGD
- Optimization 101 for Data Scientists - Aug 8, 2018.
We show how to use optimization strategies to make the best possible decision.
Football, Julia, Optimization, Python, R, Sports
Only Numpy: Implementing GANs and Adam Optimizer using Numpy - Aug 6, 2018.
This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.
GANs, Generative Adversarial Network, Neural Networks, numpy, Optimization, Python
- Simple Tips for PostgreSQL Query Optimization - Jun 22, 2018.
A single query optimization tip can boost your database performance by 100x. Although we usually advise our customers to use these tips to optimize analytic queries (such as aggregation ones), this post is still very helpful for any other type of query.
Optimization, Postgres, SQL, Statsbot
- An Intuitive Introduction to Gradient Descent - Jun 21, 2018.
This post provides a good introduction to Gradient Descent, covering the intuition, variants and choosing the learning rate.
Gradient Descent, Machine Learning, Optimization
- Optimization Using R - May 18, 2018.
Optimization is a technique for finding out the best possible solution for a given problem for all the possible solutions. Optimization uses a rigorous mathematical model to find out the most efficient solution to the given problem.
Pages: 1 2
Excel, Linear Programming, Optimization, R
Introduction to Optimization with Genetic Algorithm - Mar 14, 2018.
This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
Genetic Algorithm, K-nearest neighbors, Optimization
- Using AutoML to Generate Machine Learning Pipelines with TPOT - Jan 29, 2018.
This post will take a different approach to constructing pipelines. Certainly the title gives away this difference: instead of hand-crafting pipelines and hyperparameter optimization, and performing model selection ourselves, we will instead automate these processes.
Automated Machine Learning, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow
- Managing Machine Learning Workflows with Scikit-learn Pipelines Part 3: Multiple Models, Pipelines, and Grid Searches - Jan 24, 2018.
In this post, we will be using grid search to optimize models built from a number of different types estimators, which we will then compare and properly evaluate the best hyperparameters that each model has to offer.
Data Preprocessing, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow
- Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search - Jan 19, 2018.
Another simple yet powerful technique we can pair with pipelines to improve performance is grid search, which attempts to optimize model hyperparameter combinations.
Data Preprocessing, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow
- Custom Optimizer in TensorFlow - Jan 8, 2018.
How to customize the optimizers to speed-up and improve the process of finding a (local) minimum of the loss function using TensorFlow.
Deep Learning, Optimization, TensorFlow
- Understanding Objective Functions in Neural Networks - Nov 23, 2017.
This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.
Cost Function, Deep Learning, Gradient Descent, Neural Networks, Optimization
- TensorFlow: What Parameters to Optimize? - Nov 9, 2017.
Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.
Neural Networks, Optimization, Python, TensorFlow
Closing the Insights-to-Action Gap - Sep 5, 2017.
There are many types of analytics for getting insight out of data, but the bigger and more difficult challenge is turning that insight into action. What should we do differently based on your findings?
Analytics, Gartner, Optimization, Skills
- What Is Optimization And How Does It Benefit Business? - Aug 10, 2017.
Here we explain what Mathematical Optimisation is, and discuss how it can be applied in business and finance to make decisions.
Business, Credit Risk, Decision Making, Optimization
- Optimization in Machine Learning: Robust or global minimum? - Jun 30, 2017.
Here we discuss how convex problems are solved and optimised in machine learning/deep learning.
Deep Learning, Gradient Descent, Machine Learning, Optimization, UAI
- Introducing Dask-SearchCV: Distributed hyperparameter optimization with Scikit-Learn - May 12, 2017.
We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.
Dask, Distributed Computing, Distributed Systems, Machine Learning, Optimization, scikit-learn
- Learning to Learn by Gradient Descent by Gradient Descent - Feb 2, 2017.
What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!
Gradient Descent, Machine Learning, NIPS, Optimization
5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
Boosting, C++, Data Preparation, Decision Trees, Machine Learning, Neural Networks, Optimization, Overlook, Pandas, Python, scikit-learn
Game Theory Reveals the Future of Deep Learning - Dec 29, 2016.
This post covers the emergence of Game Theoretic concepts in the design of newer deep learning architectures. Deep learning systems need to be adaptive to imperfect knowledge and coordinating systems, 2 areas with which game theory can help.
Architecture, Deep Learning, Optimization
The hard thing about deep learning - Dec 1, 2016.
It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.
CA, Deep Learning, Neural Networks, NP-hard, Optimization, San Jose, Strata
- The Hard Problems AI Can’t (Yet) Touch - Jul 11, 2016.
It's tempting to consider the progress of AI as though it were a single monolithic entity,
advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives
AI, Machine Learning, Optimization, Reinforcement Learning, Supervised Learning
- Angoss 9.6 Data Science Software Suite - Apr 29, 2016.
Angoss software provides users with comprehensive scorecard building functionality that is fast, reliable, accurate, and business centric.
Angoss, Data Science Platform, Optimization, Tableau
- Interview: Ksenija Draskovic, Verizon on Dissecting the Anatomy of Predictive Analytics Projects - Apr 15, 2015.
We discuss Predictive Analytics use cases at Verizon Wireless, advantages of a unified data view, model selection and common causes of failure.
Customer Intelligence, Interview, Ksenija Draskovic, Optimization, Predictive Analytics, Project Fail, Use Cases, Verizon
- XLMiner solves Big Data Problems in Excel - Jun 26, 2014.
XLMiner, a part of Analytic Solver Platform integrated software for predictive and prescriptive analytics - forecasting, data mining, optimization and simulation, lets you solve small or Big Data problems in Excel.
Data Mining, Excel, Forecasting, Optimization, XLMiner
- The Algorithm that Runs the World Can Now Run More of It - Jun 13, 2014.
The most important algorithm, used for optimizing almost everything, is linear programming. New advances allow linear programming problems to be solved faster using the new commercial parallel simplex solver.
Algorithms, FICO, Linear Programming, Optimization, Qi Huangfu, Simplex
