The LazyProgrammer is a data scientist, big data engineer, and full stack software engineer. He is especially interested in deep learning and neural networks. Some also refer to this as AI, or artificial intelligence.
He graduated with a masters degree with a thesis on machine learning for brain-computer interfaces. This research would help those who are non-mobile or non-vocal communicate with their caregivers.
The LazyProgrammer got his start in machine learning and data science by learning about computational neuroscience and neural engineering. The physics aspect has always interested him but the practical nature of machine learning and data science has made up a majority of his work.
After spending years in online advertising and the media, working to build and improve big data pipelines and using machine learning to increase revenue via CTR (click-through rate) optimization and conversion tracking, he began to work for himself.
This allowed the LazyProgrammer to focus 100% of his effort on deepening his knowledge of machine learning and data science. He works with startups and larger companies to set up data pipelines and engineer predictive models that result in meaningful insights and data-driven decision making.
The LazyProgrammer also loves to teach. He has helped many adults looking to change their career path and dive into the startup and tech world. Students at General Assembly, the Flatiron School, and App Academy have all benefitted from his help. He has also helped many graduate students at various ivy leagues and other colleges through their machine learning and data science programs.
The LazyProgrammer loves to give away free tutorials and other material. You can get a FREE 6-week introduction to machine learning course by signing up for his newsletter at:
The LazyProgrammer also has a collection of Udemy courses that teach topics like machine learning, data science, and deep learning. You can find them here:
This book will teach you the fundamentals of deep learning. All of deep learning depends on one fundamental algorithm, the “secret sauce”, if you will. That is what you will learn in this book. You will learn how we get there from basic undergraduate math. You will learn how it can be modified for speed improvements. You will learn how to code it in Numpy, Theano, and TensorFlow.