Ditulis pada: January 21, 2020
Udemy Coupon - Harness the power of machine and deep learning of TensorFlow with ease
- 3.1 (6 ratings)
- Created by Packt Publishing
- English [Auto-generated]
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Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. So if you’re looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are:
Setting up TensorFlow for actual industrial use, including high-performance setup aspects like multi-GPU support
Embedded with solid projects and examples to teach you how to implement TensorFlow in production
Empower you to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage
Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with TensorBoard – all within an example-rich context using problems from multiple industries. The focus is towards introducing new concepts through problems which are coded and solved over the course of each video. You will then learn how to implement TensorFlow in production. Each project in this Learning Path provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Finally, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using TensorFlow.
On completion of this Learning Path, you will have gone through the full lifecycle of a TensorFlow solution with a practical demonstration to system setup, training, validation, to creating pipelines for real world data -- all the way to deploying solutions into a production settings.
Meet Your Expert:
We have the best works of the following esteemed authors to ensure that your learning journey is smooth:
Shams Ul Azeem is an undergraduate of NUST Islamabad, Pakistan in Electrical Engineering. He has a great interest in computer science field and started his journey from android development. Now he’s pursuing his career in machine learning, particularly in deep learning by doing medical related freelance projects with different companies. He was also a member of RISE lab, NUST and has a publication in IEEE International Conference, ROBIO as a co-author on “Designing of motions for humanoid goal keeper robots”.
Rodolfo Bonnin a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued Parallel Programming and Image Understanding postgraduate courses at Uni Stuttgart, Germany. He has done research on high-performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU and GPU supporting the neural network feedforward stage. More recently he's been working in the field of fraud pattern detection with neural networks, and is currently working on signal classification using ML techniques.
Will Ballard serves as chief technology officer at GLG and is responsible for the Engineering and IT organizations. Prior to joining GLG, Will was the executive vice president of technology and engineering at Demand Media. He graduated Magna Cum Laude with a BS in Mathematics from Claremont McKenna College.
Who this course is for:
This Learning Path is aimed at data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results using TensorFlow.
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