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Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
Approx. 34 hours to complete
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. 2) Basic linear algebra and probability....
Mathematics for Machine Learning: PCA
by Marc Peter Deisenroth- 4
Approx. 18 hours to complete
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. Good background in linear algebra (e. , matrix and vector algebra, linear independence, basis) Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization....
Machine Learning 2020: Complete Maths for Machine Learning
by Jitesh Khurkhuriya- 4.1
2.5 hours on-demand video
Learn Math for Machine Learning, Math for Data Science, Linear Algebra, Calculus, Vectors & Matrices, Probability & more This course of Math For Machine Learning, aims to bridge that gap. This course is divided into following sections, Algebra Foundations Linear Algebra Linear Algebra is the mathematics of the 21st Century....
$11.99
Modern Deep Convolutional Neural Networks with PyTorch
by Denis Volkhonskiy- 4.2
2 hours on-demand video
Dear friend, welcome to the course "Modern Deep Convolutional Neural Networks"! I tried to do my best in order to share my practical experience in Deep Learning and Computer vision with you. Matrix calculus, Linear Algebra, Probability theory and Statistics Basics of Machine Learning: Regularization, Linear Regression and Classification, Basics of Deep Learning: Linear layers, SGD, Multi-layer perceptron...
$11.99
VLSI CAD Part II: Layout
by Rob A. Rutenbar- 4.8
Approx. 24 hours to complete
You should complete the VLSI CAD Part I: Logic course before beginning this course. Linear algebra and calculus at the level of a junior or senior in engineering. Elementary knowledge of RC linear circuits (at the level of an introductory physics class). Tools For This Course End of Course Survey...
Linear Algebra III: Determinants and Eigenvalues
by Greg Mayer- 0.0
3 Weeks
This course takes you through roughly three weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology. The goal of this part of the course is to decompose the action of a linear transformation that may be visualized. Use eigenvalues to determine identify the rotation and dilation of a linear transform...
$199
Supervised Learning: Regression
by Mark J Grover , Miguel Maldonado- 4.8
Approx. 11 hours to complete
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. Introduction to Supervised Machine Learning and Linear Regression Introduction to Linear Regression...
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Supervised Machine Learning: Regression
by Mark J Grover , Miguel Maldonado- 4.7
Approx. 11 hours to complete
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. Introduction to Supervised Machine Learning and Linear Regression Introduction to Linear Regression...
Sparse Representations in Signal and Image Processing: Fundamentals
by Michael Elad , Alona Golts- 0.0
5 Weeks
This course introduces the fundamentals of the field of sparse representations, starting with its theoretical concepts, and systematically presenting its key achievements. Its core idea is the description of the data as a linear combination of few building blocks - atoms - taken from a pre-defined dictionary of such fundamental elements....
$149
Mathematical Foundations of Machine Learning
by Dr Jon Krohn- 4.5
15.5 hours on-demand video
Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch Jon Krohn, this course provides a firm grasp of the mathematics — namely the linear algebra and calculus — that underlies machine learning algorithms and data science models. Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science...
$17.99