- Baidu Research: 10 Technology Trends in 2021, by Baidu Research - Jan 29, 2021.
Understanding future technology trends may never have been as important as it is today. Check out the prediction of the 10 technology trends in 2021 from Baidu Research.
- Machine learning adversarial attacks are a ticking time bomb, by Ben Dickson - Jan 29, 2021.
Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.
- What is Graph Theory, and Why Should You Care?, by Vegard Flovik - Jan 29, 2021.
Go from graph theory to path optimization.
- Top 5 Reasons Why Machine Learning Projects Fail, by Sudeep Srivastava - Jan 28, 2021.
The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.
- Machine learning is going real-time, by Chip Huyen - Jan 28, 2021.
Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?
- Working With The Lambda Layer in Keras, by Ahmed Gad - Jan 28, 2021.
In this tutorial we'll cover how to use the Lambda layer in Keras to build, save, and load models which perform custom operations on your data.
- How to Get a Job as a Data Scientist, by Devin Partida - Jan 27, 2021.
Here’s a step-by-step guide to starting your career in data science.
- Popular Machine Learning Interview Questions, part 2, by Mo Daoud - Jan 27, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.
- Support Vector Machine for Hand Written Alphabet Recognition in R, by Mohan Rai - Jan 27, 2021.
We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.
- Is M.Tech in Data Science Worth It?, by Great Learning - Jan 26, 2021.
Is M.Tech in Data Science worth it or should you learn using just online courses and projects. Let's try to find the answer to that question.
- What to Learn to Become a Data Scientist in 2021, by Andrea Laura - Jan 26, 2021.
As data becomes the new ‘Gold’ for businesses, data scientists are set to find their value in this gold. This write-up clearly defines the job requirements and company expectations that this phenomenally evolving role entails.
- Want to Be a Data Scientist? Don’t Start With Machine Learning, by Terence Shin - Jan 26, 2021.
Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.
- Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI, by Craig Smith - Jan 26, 2021.
Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.
- Six Times Bigger than GPT-3: Inside Google’s TRILLION Parameter Switch Transformer Model, by Jesus Rodriguez - Jan 25, 2021.
Google’s Switch Transformer model could be the next breakthrough in this area of deep learning.
- The Ultimate Scikit-Learn Machine Learning Cheatsheet, by Andre Ye - Jan 25, 2021.
With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.
- Top Stories, Jan 18-24: How I Got 4 Data Science Offers and Doubled my Income 2 Months After Being Laid Off; Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants - Jan 25, 2021.
Also: Data Engineering — the Cousin of Data Science, is Troublesome; Build a Data Science Portfolio that Stands Out Using These Platforms; K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; Popular Machine Learning Interview Questions
- Null Hypothesis Significance Testing is Still Useful, by Nicole Janeway Bills - Jan 25, 2021.
Even in the aftermath of the replication crisis, statistical significance lingers as an important concept for Data Scientists to understand.
- Building a Deep Learning Based Reverse Image Search, by Vegard Flovik - Jan 22, 2021.
Following the journey from unstructured data to content based image retrieval.
- Data Engineering — the Cousin of Data Science, is Troublesome, by Lissie Mei - Jan 22, 2021.
A Data Scientist must be a jack of many, many trades. Especially when working in broader teams, understanding the roles of others, such as data engineering, can help you validate progress and be aware of potential pitfalls. So, how can you convince your analysts to realize the importance of expanding their toolkit? Examples from real life often provide great insight.
- Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants, by George Vyshnya - Jan 22, 2021.
Kaggle’s survey of ‘State of Data Science and Machine Learning 2020’ covers a lot of diverse topics. In this post, we are going to look at the popularity of cloud computing platforms and products among the data science and ML professionals participated in the survey.
- How to Use MLOps for an Effective AI Strategy, by Sigmoid - Jan 21, 2021.
The need to deal with the challenges and other smaller nuances of deploying machine learning models has given rise to the relatively new concept of MLOps. – a set of best practices aimed at automating the ML lifecycle, bringing together the ML system development and ML system operations.
- Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning, by Martin Isaksson - Jan 21, 2021.
Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?
- Top 5 Artificial Intelligence (AI) Trends for 2021, by Kevin Vu - Jan 21, 2021.
From voice and language driven AI to healthcare, cybersecurity and beyond, these are some of the key AI trends for 2021.
- Travel to faster, trusted decisions in the cloud, by SAS - Jan 20, 2021.
Join technology experts, partners and analysts in the industry to see what is taking off in AI, cloud computing and putting models into production for better outcomes and trusted results. Register today!
- Mastering TensorFlow Variables in 5 Easy Steps, by Orhan G. Yalçın - Jan 20, 2021.
Learn how to use TensorFlow Variables, their differences from plain Tensor objects, and when they are preferred over these Tensor objects | Deep Learning with TensorFlow 2.x.
- Popular Machine Learning Interview Questions, by Mo Daoud - Jan 20, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.
- Loglet Analysis: Revisiting COVID-19 Projections, by Dennis Ganzaroli - Jan 20, 2021.
We will show that the decomposition of growth into S-shaped logistic components also known as Loglet analysis, is more accurate as it takes into account the evolution of multiple covid waves.
- Graph Representation Learning: The Free eBook, by Matthew Mayo - Jan 19, 2021.
This free eBook can show you what you need to know to leverage graph representation in data science, machine learning, and neural network models.
- Build a Data Science Portfolio that Stands Out Using These Platforms, by Benjamin Obi Tayo - Jan 19, 2021.
Making your big break into the data science profession means standing out to potential employers in a crowd of tough competition. An important way to showcase your skills and experience is through the presentation of a portfolio. Following these recommendations for developing your portfolio will help you network effectively and stay on top of an ever-changing field.
- How I Got 4 Data Science Offers and Doubled my Income 2 Months After Being Laid Off, by Emma Ding - Jan 19, 2021.
In this blog, I shared my story on getting 4 data science job offers including Airbnb, Lyft and Twitter after being laid off. Any data scientist who was laid off due to the pandemic or who is actively looking for a data science position can find something here to which they can relate.
- Data Science and Analytics Career Trends for 2021, by Great Learning - Jan 18, 2021.
Let's check out what are the new data science and analytics career trends for 2021 that may also shape the career options in the future.
- Microsoft Uses Transformer Networks to Answer Questions About Images With Minimum Training, by Jesus Rodriguez - Jan 18, 2021.
Unified VLP can understand concepts about scenic images by using pretrained models.
- Top Stories, Jan 11-17: K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; My Data Science Learning Journey So Far - Jan 18, 2021.
Also: Essential Math for Data Science: Information Theory; Cleaner Data Analysis with Pandas Using Pipes; The Four Jobs of the Data Scientist
- Can Data Science Be Agile? Implementing Best Agile Practices to Your Data Science Process, by Jerzy Kowalski - Jan 18, 2021.
Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.
- Comprehensive Guide to the Normal Distribution, by Nicole Janeway Bills - Jan 18, 2021.
Drop in for some tips on how this fundamental statistics concept can improve your data science.
- Snowflake and Saturn Cloud Partner To Bring 100x Faster Data Science to Millions of Python Users, by Saturn Cloud - Jan 15, 2021.
Snowflake the cloud data platform, is partnering, integrating products, and pursuing a joint go-to-market with Saturn Cloud to help data science teams get 100x faster results. Read more about developments and how to get started here.
- Essential Math for Data Science: Information Theory, by Hadrien Jean - Jan 15, 2021.
In the context of machine learning, some of the concepts of information theory are used to characterize or compare probability distributions. Read up on the underlying math to gain a solid understanding of relevant aspects of information theory.
- K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines, by Jakub Adamczyk - Jan 15, 2021.
K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.
- Cleaner Data Analysis with Pandas Using Pipes, by Soner Yildirim - Jan 15, 2021.
Check out this practical guide on Pandas pipes.
- 8 New Tools I Learned as a Data Scientist in 2020, by Ben Weber - Jan 14, 2021.
The author shares the data science tools learned while making the move from Docker to Live Deployments.
- Data Cleaning and Wrangling in SQL, by Antonio Badia - Jan 14, 2021.
SQL is a foundational skill for data analysts but its application is sometimes limited within the data pipeline. However, SQL can be successfully used for many pre-processing tasks, such as data cleaning and wrangling, as demonstrated here by example.
- Unsupervised Learning for Predictive Maintenance using Auto-Encoders, by Kundaliya & Aggarwal - Jan 14, 2021.
This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; Approaches for machine diagnosis; Machine diagnosis using machine learning
- My Data Science Learning Journey So Far, by Arnuld on Data - Jan 13, 2021.
These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.
- The Four Jobs of the Data Scientist, by Roger Peng - Jan 13, 2021.
So, what do you do for a living? Sometimes, the answer to that question can feel like, "everything!" Well, for the Data Scientist, an extreme sense of being a "jack of all trades" is common. In fact, four such trades can be defined that a top-quality Data Scientist will iterate through during any one project.
- The Best Tool for Data Blending is KNIME, by Dennis Ganzaroli - Jan 13, 2021.
These are the lessons and best practices I learned in many years of experience in data blending, and the software that became my most important tool in my day-to-day work.
- Creating Good Meaningful Plots: Some Principles, by Vikrant Dogra - Jan 12, 2021.
Hera are some thought starters to help you create meaningful plots.
- Working With Sparse Features In Machine Learning Models, by Arushi Prakash - Jan 12, 2021.
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.
- Cloud Data Warehouse is The Future of Data Storage, by Nitin Kumar - Jan 12, 2021.
Today, cloud data storage accounts for 45% of all enterprise data and by Q2 2021, that number could grow to 53%. Now is the time to embrace cloud than now.
- Top Stories, Jan 04-10: Best Python IDEs and Code Editors You Should Know; All Machine Learning Algorithms You Should Know in 2021 - Jan 11, 2021.
Also: DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created; 10 Underappreciated Python Packages for Machine Learning Practitioners; Six Tips on Building a Data Science Team at a Small Company
- 5 Tools for Effortless Data Science, by Nicole Janeway Bills - Jan 11, 2021.
The sixth tool is coffee.
- Attention mechanism in Deep Learning, Explained, by Nagesh Chauhan - Jan 11, 2021.
Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.
- OpenAI Releases Two Transformer Models that Magically Link Language and Computer Vision, by Jesus Rodriguez - Jan 11, 2021.
OpenAI has released two new transformer architectures that combine image and language tasks in an fun and almost magical way. Read more about them here.
- JupyterLab 3 is Here: Key reasons to upgrade now, by Matthew Mayo - Jan 8, 2021.
Read about these 3 reasons for checking out JupyterLab 3 today.
- Best Python IDEs and Code Editors You Should Know, by Claire D. Costa - Jan 8, 2021.
Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE -- that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider, and are reviewed with their noteworthy features.
- Top 10 Computer Vision Papers 2020, by Louis (What’s AI) Bouchard - Jan 8, 2021.
The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.
- Top December Stories: Why the Future of ETL Is Not ELT, But EL(T); 20 Core Data Science Concepts for Beginners - Jan 7, 2021.
Also: A Rising Library Beating Pandas in Performance; 15 Free Data Science, Machine Learning & Statistics eBooks for 2021
- 11 Industrial AI Trends that will Dominate the World in 2021, by Swati Giri - Jan 7, 2021.
These trends broadly cover the three themes of: Where will businesses adopt AI in 2021? How will AI become more accessible? How will AI capabilities evolve?
- Advice to aspiring Data Scientists – your most common questions answered, by Roman Orac - Jan 7, 2021.
Embarking on a new career path can be daunting with many unknowns about how to get started and how to be successful. If you are aspiring to become a Data Scientist, then the answers to these common questions can help set you off on the right foot.
- 10 Underappreciated Python Packages for Machine Learning Practitioners, by Vinay Uday Prabhu - Jan 7, 2021.
Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.
- CatalyzeX: A must-have browser extension for machine learning engineers and researchers, by Himanshu Ragtah - Jan 6, 2021.
CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites).
- Learn Data Science for free in 2021, by Ahmad Anis - Jan 6, 2021.
If you are considering starting a career path in machine learning and data science, then there is a great deal to learn theoretically, along with gaining practical skills in applying a broad range of techniques. This comprehensive learning plan will guide you to start on this path, and it is all available for free.
- MLOps: Model Monitoring 101, by Saha & Bose - Jan 6, 2021.
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.
- Where is Marketing Data Science Headed?, by Kevin Gray - Jan 5, 2021.
Marketing data science - data science related to marketing - is now a significant part of marketing. Some of it directly competes with traditional marketing research and many marketing researchers may wonder what the future holds in store for it.
- How to Get a Job as a Data Engineer, by Anna Anisienia - Jan 5, 2021.
Data engineering skills are currently in high demand. If you are looking for career prospects in this fast-growing profession, then these 10 skills and key factors will help you prepare to land an entry-level position in this field.
- Model Experiments, Tracking and Registration using MLflow on Databricks, by Dash Desai - Jan 5, 2021.
This post covers how StreamSets can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps, and demonstrates integration with Databricks and MLflow.
- DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created, by Jesus Rodriguez - Jan 4, 2021.
MuZero takes a unique approach to solve the problem of planning in deep learning models.
- Top Stories, Dec 21 – Jan 03: Monte Carlo integration in Python; 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - Jan 4, 2021.
Also: SQL vs NoSQL: 7 Key Takeaways; Generating Beautiful Neural Network Visualizations; Meet whale! The stupidly simple data discovery tool; Key Data Science Algorithms Explained: From k-means to k-medoids clustering
- All Machine Learning Algorithms You Should Know in 2021, by Terence Shin - Jan 4, 2021.
Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.
- Six Tips on Building a Data Science Team at a Small Company, by Zbar & Vallejo - Jan 4, 2021.
When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you're the data scientist hired to make this happen, we have some tips to help you face the task head-on.