How To Solve Almost Any Problem
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How to Solve Almost Any Problem
Problems block and slow down your progress; here’s how to overcome them–simply, efficiently and effectively. This book offers straightforward, empowering science-based solutions to problems, big and small, at work or in life. It takes a never before seen approach to problem solving, powerfully combining lessons from cognitive science, established problem-solving theory and vast practical experience. It includes a radical new approach to analysing problems: The Problem Matrix. This will transform your approach to problems, challenge your thinking and help you develop new, positive, solution-focussed mindsets for the long-term.
How to Solve Almost Any Problem
Problems block and slow down your progress; here’s how to overcome them–simply, efficiently and effectively. This book offers straightforward, empowering science-based solutions to problems, big and small, at work or in life. It takes a never before seen approach to problem solving, powerfully combining lessons from cognitive science, established problem-solving theory and vast practical experience. It includes a radical new approach to analysing problems: The Problem Matrix. This will transform your approach to problems, challenge your thinking and help you develop new, positive, solution-focussed mindsets for the long-term.
Approaching (Almost) Any Machine Learning Problem
This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub