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Linear Algebra for Machine Learning /
6.5 Hours of Video Instruction An introduction to the linear algebra behind machine learning models Overview Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications. About the Instr...
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
Addison-Wesley Professional,
2020.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | 6.5 Hours of Video Instruction An introduction to the linear algebra behind machine learning models Overview Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications. About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated (Addison-Wesley, 2020), an instant #1 bestseller that has been translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University and New York University, as well as online via O'Reilly, YouTube, and the Super Data Science Podcast. Jon holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times. Skill Level Intermediate Learn How To Appreciate the role of algebra in machine and deep learning Understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces Develop a geometric intuition of what's going on beneath the hood of machine learning algorithms, including those used for deep learning Be able to more intimately grasp the details of machine learning papers as well as all of the other subjects that underlie ML, including calculus, statistics, and optimization algorithms Manipulate tensors of all dimensionalities including scalars, vectors, and matrices, in all of the leading Python tensor libraries: NumPy, TensorFlow, and PyTorch Reduce the dimensionality of complex spaces down to their most informative elements with techniques such as eigendecomposition (eigenvectors and eigenvalues), singular value decomposition, and principal components analysis Who Should Take This Course Users of high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms who would now like to understand the fundamentals underlying the abstractions, enabling them to expand their capabilities Software developers who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems Data scientists who would like to reinforce their understanding of the subjects at the core of their professional discipline Data analysts or AI enthusiasts who would like to become a data scientist or data/ML engineer and are keen to deeply und ... |
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| Descripción Física: | 1 online resource (1 video file, approximately 6 hr., 33 min.) |
| ISBN: | 9780137398119 0137398115 |