LEARNING PATH: TensorFlow: RealWorld Solutions to TensorFlow | Data Science Online Course by Udemy

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Build deployable solutions and tackle common machine learning problems with ease using Googles TensorFlow library

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LEARNING PATH: TensorFlow: RealWorld Solutions to TensorFlow

TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. TensorFlow is an open source software library 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. Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models. If you’re interested to hone your skills in building deployable solutions with Googles TensorFlow library, then go for this Learning Path. Packts 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: Deep dive into training, validation, and monitoring training performance Build simple TensorFlow graphs for everyday computations Design and train a multilayer neural network with TensorFlow This Learning Path addresses common commercial machine learning problems using Googles TensorFlow library. It will not only help you discover what TensorFlow is and how to use it, but also show you the unbelievable things that can be done in machine learning with the help of real-world examples and use cases. Youll start off with the basic installation of TensorFlow, moving on to covering the unique features of the library such as data flow graphs, training, and visualization of performance with TensorBoardall within an example-rich context using problems from multiple sources. The focus is on introducing new concepts through problems that are coded and solved over the course of each section. Further, youll dive deeper into the hidden layers of abstraction using raw data. This Learning Path will teach you various complex algorithms for deep learning and various examples that use these deep neural networks. Youll also learn how to train your machine to craft new features to make sense of deeper layers of data. Youll come across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, high level interfaces, and more. With the help of novel practical examples, youll become an ace at advanced multilayer networks, image recognition, and beyond. Towards the end of this Learning Path, youll be able to process data and gain insights that will change the way you look at it, with the efficiency and simplicity of TensorFlow Meet Your Experts: We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad, Pakistan. He has a great interest in the computer science field, and he started his journey with Android development. Now, hes pursuing his career in machine learning, particularly in deep learning, by doing medical-related freelancing projects with different companies. He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE International Conference, ROBIO as a co-author of designing of motions for humanoid goalkeeper robots. Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for Dan Does Data, a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research articles and presented findings at the transportation research board and other academic journals.

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