Convolutional Neural Networks

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In case you haven’t studied engineering or mathematics, this section is designed to help you understand all the mathematical concepts used in data science such as Matrix, Scalars, Vectors, Linear Algebra, Tensor, and Geometry.The section ends with the course teaching users how to perform several operations on Matrix such as Addition, Subtraction, and how to Transpose a matrix. It also helps you resolve the several errors experienced when adding a matrix. Deep LearningThe last section of the course teaches users about Deep Learning.

It starts with a video titled “What to expect from this part?”, followed by an introduction to what Neural Networks are. You will also Europe Mobile Number List learn how to build a simple Neural Network using NumPy.Also, you will also learn about what TensorFlow does and how to use it for various purposes.It then proceeds with the introduction of Deep Neural Networks. During the process, you will also learn how to install Glorot, also known as Xavier. The course lets users get familiar with Preprocessing and help classify on the MNIST Dataset.




It also helps users apply the knowledge learned using an example. The section ends with a summary of whatever you have learned and an overview of . Case StudiesThere are several case studies included in the course includingreprocessing the “Absenteeism_data”Applying Machine Learning to Create the “Absenteeism_module”Loading the “Absenteeism_module”Analyzing the Predicted Outputs in TableauPricing:The course is priced at approximately $ Taking into consideration the content of the course, it is reasonably priced.