- Generalization in Neural Networks - Nov 18, 2019.
When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.
Tags: Complexity, Deep Learning, Dropout, Neural Networks, Overfitting, Regularization, Training Data
- 5 Fundamental AI Principles - Oct 3, 2019.
While AI may appear magical at times, these five principles will help guide you to avoid pitfalls when leveraging this tech.
Tags: AI, Data Cleaning, Deployment, Training Data
- 6 Tips for Building a Training Data Strategy for Machine Learning - Sep 2, 2019.
Without a well-defined approach for collecting and structuring training data, launching an AI initiative becomes an uphill battle. These six recommendations will help you craft a successful strategy.
Tags: Advice, Machine Learning, Training Data
6 Key Concepts in Andrew Ng’s “Machine Learning Yearning” - Aug 12, 2019.
If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.
Tags: AI, Andrew Ng, Best Practices, Deployment, Machine Learning, Metrics, Training Data
- Overview of Different Approaches to Deploying Machine Learning Models in Production - Jun 12, 2019.
Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.
Tags: Deployment, Jupyter, Machine Learning, Production, Training Data
- How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks - May 30, 2019.
The training of machine learning models is often compared to winning the lottery by buying every possible ticket. But if we know how winning the lottery looks like, couldn’t we be smarter about selecting the tickets?
Tags: Deep Learning, Lottery, Machine Learning, Neural Networks, Training Data
- Acquiring Labeled Data to Train Your Models at Low Costs - Feb 27, 2019.
We discuss groundbreaking and unique methods to acquire labeled data at low cost, including 3rd-Party Plug-and-Play AI Model, Zero-Shot Learning, and Restructuring the Existing Data Set.
Tags: Data Preparation, Machine Learning, ParallelDots, Training Data, Transfer Learning
- What to do when your training and testing data come from different distributions - Jan 4, 2019.
However, sometimes only a limited amount of data from the target distribution can be collected. It may not be sufficient to build the needed train/dev/test sets. What to do in such a case? Let us discuss some ideas!
Tags: Distribution, Machine Learning, Training Data
- Key Takeaways from AI Conference SF, Day 2: AI and Security, Adversarial Examples, Innovation - Oct 30, 2018.
Highlights and key takeaways from selected keynote sessions on day 2 of AI Conference San Francisco 2018.
Tags: Adversarial, AI, Architecture, GPU, O'Reilly, Privacy, San Francisco, TPU, Training Data
- KDnuggets™ News 18:n23, Jun 13: Did Python declare victory over R?; Master the Netflix Interview; Deep Learning Projects DIY Style - Jun 13, 2018.
Also: Command Line Tricks For Data Scientists; How (dis)similar are my train and test data?; 5 Machine Learning Projects You Should Not Overlook, June 2018; Introduction to Game Theory; Human Interpretable Machine Learning
Tags: Data Science, Deep Learning, Interview questions, Machine Learning, Netflix, Python, R, Training Data
- Why you need to improve your training data, and how to do it - Jun 11, 2018.
This article examines the way you need to improve your training data and how it can be accomplished, including speech commands, choosing the right data, picking a model fast and more.
Pages: 1 2
Tags: AI, Andrej Karpathy, Machine Learning, Training Data
- How (dis)similar are my train and test data? - Jun 7, 2018.
This articles examines a scenario where your machine learning model can fail.
Tags: Data Science, Datasets, Feature Selection, Machine Learning, Training Data
- How to Organize Data Labeling for Machine Learning: Approaches and Tools - May 16, 2018.
The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.
Pages: 1 2
Tags: Altexsoft, Crowdsourcing, Data Preparation, Image Recognition, Machine Learning, Training Data
- Learning Curves for Machine Learning - Jan 17, 2018.
But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves.
Pages: 1 2
Tags: Bias, Machine Learning, Metrics, Training Data, Variance
- How (and Why) to Create a Good Validation Set - Nov 24, 2017.
The definitions of training, validation, and test sets can be fairly nuanced, and the terms are sometimes inconsistently used. In the deep learning community, “test-time inference” is often used to refer to evaluating on data in production, which is not the technical definition of a test set.
Tags: Cross-validation, Datasets, Rachel Thomas, Training Data, Validation
- How to squeeze the most from your training data - Jul 27, 2017.
In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.
Tags: Data Analysis, Data Preparation, Machine Learning, Support Vector Machines, SVM, Training Data
- 7 Ways to Get High-Quality Labeled Training Data at Low Cost - Jun 13, 2017.
Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.
Tags: Crowdsourcing, Data Preparation, Gamification, Machine Learning, Training Data
- Do We Need More Training Data or More Complex Models? - Mar 23, 2015.
Do we need more training data? Which models will suffer from performance saturation as data grows large? Do we need larger models or more complicated models, and what is the difference?
Tags: Big Data, convnet, Generalized Linear Models, K-nearest neighbors, Training Data, Zachary Lipton
10 Free Top Notch Machine Learning Courses