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The online classes, which are available on the Udacity learning platform, have been developed as a subdivision of the Machine Learning Engineer Nanodegree program, aimed at enabling students to devise intelligent systems and algorithms, that can process vast amounts of data in order to formulate conclusions and accurate predictions.
The person who has been in charge with conceiving the study material was Vincent Vanhoucke, tech lead at Google Brain, and principal scientist at the company.
He has been working in conjunction with Arpan Chakraborty, course developer at Udacity, with a PhD in computer science, awarded by North Carolina State University.
Together, they have created a highly challenging and comprehensive set of instructionals, which includes video lectures (with factual, up-to-date information and examples), as well as exercises, practical applications and projects.
The purpose of this self-paced course is to assist students in gaining more insight into the promising and ever-expanding field represented by machine learning and artificial intelligence.
The focus will be on deep learning, through which complex, vast amounts of data can be interpreted and processed, by arranging them layer by layer into hierarchies, depending on their level of abstraction.
Through the clips presented by Vincent Vanhoucke, it will be possible to find out how to use artificial neural networks, including convolutional ones, that are commonly employed for image recognition, natural language processing or video analysis.
There will also be detailed information regarding long short-term memory neural networks, which have proven reliable and efficient in speech recognition, and have also yielded impressive results when it comes to intelligent word recognition.
The class assignments and various tasks and projects will also introduce students to TensorFlow, an open-source machine learning library developed by Google.
The level of difficulty for this newly launched deep learning course is considered to lie between medium and advanced, given the fact that the material is mostly addressed to data scientists or engineers who have already completed prior training in machine learning, and who have become acquainted with supervised learning strategies and tasks.
Furthermore, in order for students to actually fare well and fully comprehend all the material from the video presentations, another one of the prerequisites for this course consists in having had at least 2 years’ worth of experience in computer programming (so as to be familiar especially with the Python programming language).
In addition, it is necessary for would-be pupils to have used source code management systems such as Git and Github before, given the fact that assignments will be stored in such repositories.
Moreover, those who sign up for this course should be knowledgeable regarding linear algebra (especially matrices and vectors), calculus (partial derivatives of multivariable functions, differentiation and integration), as well as statistics (standard deviations, variance, means, modes and medians).
Those who possess all the above-mentioned skills and qualifications should be aware that they will have to devote plenty of time to this course, in order to complete it.
Generally, it’s possible to go through all the study material and finish the entire set of exercises and projects in approximately 3 months, provided that one allots at least 6 hours per week to this new endeavor.
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