Deep Learning, NLP, NMT, AI, ML
Last Updated on August 6, 2019 20 Tips, Tricks and Techniques That You Can Use ToFight Overfitting and Get Better Generalization How can you get better performance from your deep learning model? It is one of the most common questions I get asked. It might be asked as: How can I improve accuracy? …or it may be reversed as: What can I do if my neural network performs poorly? I often reply with “I don’t know exactly, but I have […]
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Last Updated on April 24, 2020 XGBoost With Python Mini-Course. XGBoost is an implementation of gradient boosting that is being used to win machine learning competitions. It is powerful but it can be hard to get started. In this post, you will discover a 7-part crash course on XGBoost with Python. This mini-course is designed for Python machine learning practitioners that are already comfortable with scikit-learn and the SciPy ecosystem. Kick-start your project with my new book XGBoost With Python, […]
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Last Updated on August 3, 2020 From Developer to Machine Learning Practitioner in 14 Days Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step […]
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Last Updated on August 12, 2019 Why Do You Get Different Results On Different Runs Of An Algorithm With The Same Data? Applied machine learning is a tapestry of breakthroughs and mindset shifts. Understanding the role of randomness in machine learning algorithms is one of those breakthroughs. Once you get it, you will see things differently. In a whole new light. Things like choosing between one algorithm and another, hyperparameter tuning and reporting results. You will also start to see the abuses […]
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Last Updated on September 30, 2016 5 Best Practices For Operationalizing Machine Learning. Not all predictive models are at Google-scale. Sometimes you develop a small predictive model that you want to put in your software. I recently received this reader question: Actually, there is a part that is missing in my knowledge about machine learning. All tutorials give you the steps up until you build your machine learning model. How could you use this model? In this post, we look at […]
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Last Updated on October 3, 2016 The 5 Most Common Mistakes That Beginners MakeAnd How To Avoid Them. I help beginners get started in machine learning. But I see the same mistakes in both mindset and action again and again. In this post, you will discover the 5 most common ways that I see beginners slip-up when getting started in machine learning. I firmly believe thatanyone can get started and do really wellwith applied machine learning. Hopefully, you can identify yourself […]
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Last Updated on August 12, 2019 You Don’t Have To Implement Algorithms…if you’re a beginner and just getting started. Stop. Are you implementing a machine learning algorithm at the moment? Why? Implementing algorithms from scratch is one of the biggest mistakes I see beginners make. In this post you will discover: The algorithm implementation trap that beginners fall into. The very real difficulty of engineering world-class implementations of machine learning algorithms. Why you should be using off-the-shelf implementations. Kick-start your […]
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Per Went From Developer To Machine Learning Practitioner,And So Can You! Per Borgen is an inspiration. He transitioned from developer to machine learning practitioner. And he explained how he did it. In this post, you will discover the lessons learned by Per on his transition. You will discover two methodologies he adopted and how you can use them. And you will discover the advice Per has for beginners, like you, that are also looking to make the transition. And you […]
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5-Steps To Get Started and Get Good at Machine Learning I teach a 5-step process that you can use to get your start in applied machine learning. It is unconventional. The traditional way to teach machine learning is bottom-up. Start with the theory and math, then algorithm implementations, then send you off to figure out how to start solving real-world problems. The traditional approach to getting started in machine learning has a gap on the path to practitioner. The Machine […]
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Last Updated on December 11, 2019 You must know how to load data before you can use it to train a machine learning model. When starting out, it is a good idea to stick with small in-memory datasets using standard file formats like comma separated value (.csv). In this tutorial you will discover how to load your data in Python from scratch, including: How to load a CSV file. How to convert strings from a file to floating point numbers. […]
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