Topic: Machine Learning
This page features most recent and most popular posts on Machine Learning.
Latest posts on Machine Learning
- Transfer Learning for Image Recognition and Natural Language Processing - Jan 14, 2022Read the second article in this series on Transfer Learning, and learn how to apply it to Image Recognition and Natural Language Processing.
- A (Much) Better Approach to Evaluate Your Machine Learning Model - Jan 12, 2022Using one or two performance metrics seems sufficient to claim that your ML model is good — chances are that it’s not.
- Interpretable Neural Networks with PyTorch - Jan 11, 2022Learn how to build feedforward neural networks that are interpretable by design using PyTorch.
- A Full End-to-End Deployment of a Machine Learning Algorithm into a Live Production Environment - Jan 11, 2022How to use scikit-learn, pickle, Flask, Microsoft Azure and ipywidgets to fully deploy a Python machine learning algorithm into a live, production environment.
- What is Transfer Learning? - Jan 5, 2022During transfer learning, the knowledge leveraged and rapid progress from a source task is used to improve the learning and development to a new target task. Read on for a deeper dive on the subject.
Most popular (badge-winning) recent posts on Machine Learning
- 6 Predictive Models Every Beginner Data Scientist Should Master [Gold Blog]Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.
- Design Patterns for Machine Learning Pipelines [Silver Blog]ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.
- What Google Recommends You do Before Taking Their Machine Learning or Data Science Course [Silver Blog]First steps to learning data science & machine learning are the foundations.
- Machine Learning Model Development and Model Operations: Principles and Practices [Gold Blog]The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.
- Introduction to AutoEncoder and Variational AutoEncoder (VAE) [Silver Blog]Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.
- Deploying Your First Machine Learning API [Silver Blog]Effortless way to develop and deploy your machine learning API using FastAPI and Deta.
- The 20 Python Packages You Need For Machine Learning and Data Science [Gold Blog]Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.
- 20 Machine Learning Projects That Will Get You Hired [Silver Blog]If you want to break into the machine learning and data science job market, then you will need to demonstrate the proficiency of your skills, especially if you are self-taught through online courses and bootcamps. A project portfolio is a great way to practice your new craft and offer convincing evidence that an employee should hire you over the competition.
- Nine Tools I Wish I Mastered Before My PhD in Machine Learning [Gold Blog]Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.
- How to Find Weaknesses in your Machine Learning Models [Gold Blog]FreaAI: a new method from researchers at IBM.
- The Machine & Deep Learning Compendium Open Book [Gold Blog]After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.
- Top 18 Low-Code and No-Code Machine Learning Platforms [Silver Blog]Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.
- Learning Data Science and Machine Learning: First Steps After The Roadmap [Silver Blog]Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.
- 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks [Gold Blog]While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems.
- Design patterns in machine learning [Silver Blog]Can we abstract best practices to real design patterns yet?
- Advice for Learning Data Science from Google’s Director of Research [Silver Blog]Surfing the professional career wave in data science is a hot prospect for many looking to get their start in the world. The digital revolution continues to create many exciting new opportunities. But, jumping in too fast without fully establishing your foundational skills can be detrimental to your success, as is suggested by this advice for data science newbies from Peter Norvig, the Director of Research at Google.
- How I Doubled My Income with Data Science and Machine Learning [Gold Blog]Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.
- Data Scientist, Data Engineer & Other Data Careers, Explained [Platinum Blog]In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.
- DeepMind Wants to Reimagine One of the Most Important Algorithms in Machine Learning [Silver Blog]In one of the most important papers this year, DeepMind proposed a multi-agent structure to redefine PCA.
- Data Science Books You Should Start Reading in 2021 [Gold Blog]Check out this curated list of the best data science books for any level.
- How to deploy Machine Learning/Deep Learning models to the web [Gold Blog]The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.
