Learning Path:TensorFlow: The Road to TensorFlow-2nd Edition

Learning Path:TensorFlow: The Road to TensorFlow-2nd Edition Udemy Course

Learning Path:TensorFlow: The Road to TensorFlow-2nd Edition Course Created by Packt Publishing.

Learning Path:TensorFlow: The Road to TensorFlow-2nd Edition has 4.0 rating out of 5 based on 74 students. Currently this course has 1,010 students. Course langwage is English.

Learning Path:TensorFlow: The Road to TensorFlow-2nd Edition Course Description

Packt's Video Learning Path is a series of individual video products put together in a logical and step-by-step fashion so that each video builds on the skills learned from previous videos.

Getting started with machine learning can be challenging, especially as new frameworks like TensorFlow start to gain traction across the enterprise. TensorFlow is an open source software library for numerical computation using dataflow graphs. Its flexible architecture allows you to deploy compute to one or more CPUs or GPUs on a desktop, server, or mobile device using a single API.

This learning path begins with a mastery of Python, with an emphasis on unraveling its secrets. Then understand deep learning implemented by Python and TensorFlow. Finally, we use TensorFlow to solve common commercial machine learning problems.

If you haven't been exposed to one of the most important trends influencing the way we do data science in the next few years, this learning path will help you speed up.

The goal of this training course is to help you understand deep learning and machine learning by learning Python first and then TensorFlow.

This learning path has been written by top experts in their field.

About the author

Daniel Arbuckle

Daniel Arbuckle has a PhD. He studied computer science at the University of Southern California. He has published numerous papers, along with several books and video courses, and is a computer science teacher and professional programmer.

Eder Santana

Eder Santana is a PhD. Electrical and computer engineering candidates. After 3 years of working with kernel machines (SVM, information theory learning, etc), Eder moved into deep learning 2.5 years ago when he started learning Theano, Caffe and other machine learning frameworks. Eder now contributes to Keras, a deep learning library for Python. In addition to deep learning, he also likes data visualization and teaches machine learning as an online forum or assistant teacher.

Dan Van Voxel

Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is known for "Dan Dos Data," a YouTube live stream that shows the power and pitfalls of neural networks. He developed new statistical models of machine learning and applied them to topics such as calculating truck traffic on highways, detecting travel time outliers, and other areas. Dan has also published and published research findings on the Transportation Research Council and other academic journals.

Shams Wool Azim

Shams Ul Azeem is an undergraduate electrical engineering student from NUST Islamabad, Pakistan. He has worked with several companies and freelanced healthcare projects to build his career in machine learning, particularly deep learning.

Packt has been dedicated to developer learning since 2004. A lot has changed in software since then, but Packt will continue to respond to these changes, looking forward to the trends and tools that will define the way we work and live. And how to make them work.

With a vast library of content, including more than 4,000 books and video tutorials, Packt's mission is to help developers stay relevant in a fast-changing world. From new web frameworks and programming languages ​​to cutting-edge data analytics to DevOps, Packt guides software professionals in every discipline to what's important to them today.

From technology that helps you develop your career and secure your future, to instant solutions to everyday technical problems, Packt is a resource to help you become a better, smarter developer.

Packt Udemy courses continue this tradition by providing comprehensive, concise video courses directly from experts.

Learning Path:TensorFlow: The Road to TensorFlow-2nd Edition Course for

• This course is ideal for Python professionals looking to familiarize themselves with deep learning and machine learning. No commercial domain knowledge is required but familiarity with Python and matrix math is expected.

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