TensorFlow in Practice Specialization

Start Date: 10/13/2019

Course Type: Specialization Course

Course Link: https://www.coursera.org/specializations/tensorflow-in-practice

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About Course

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. Courses 1-3 are available now, with Course 4 launching in July.

Course Syllabus

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Convolutional Neural Networks in TensorFlow
Natural Language Processing in TensorFlow
Sequences, Time Series and Prediction

Deep Learning Specialization on Coursera

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Article Example
TensorFlow TensorFlow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations which such neural networks perform on multidimensional data arrays. These multidimensional arrays are referred to as "tensors". In June 2016, Google's Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google.
TensorFlow TensorFlow is Google Brain's second generation machine learning system, released as open source software on November 9, 2015. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA extensions for general-purpose computing on graphics processing units). TensorFlow is available on 64-bit Linux, macOS, and mobile computing platforms including Android and iOS.
TensorFlow TensorFlow provides a Python API, as well as somewhat less documented C++, Java and Go APIs.
TensorFlow Among the applications for which TensorFlow is the foundation, are automated image captioning software, such as DeepDream. Google officially implemented RankBrain on 26 October 2015, backed by TensorFlow. RankBrain now handles a substantial number of search queries, replacing and supplementing traditional static algorithm based search results.
TensorFlow Google assigned multiple computer scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more robust application-grade library, which became TensorFlow. In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition.
TensorFlow TensorFlow is an open source software library for machine learning across a range of tasks, and developed by Google to meet their needs for systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning which humans use. It is currently used for both research and production at Google products,   often replacing the role of its closed-source predecessor, DistBelief. TensorFlow was originally developed by the Google Brain team for internal Google use before being released under the Apache 2.0 open source license on November 9, 2015.
Specialization (functional) Specialization is when people specialize in one thing or another which they are good at.
Specialization (functional) Adam Smith described economic specialization in his classic work, "The Wealth of Nations".
Specialization (pre)order In the branch of mathematics known as topology, the specialization (or canonical) preorder is a natural preorder on the set of the points of a topological space. For most spaces that are considered in practice, namely for all those that satisfy the T separation axiom, this preorder is even a partial order (called the specialization order). On the other hand, for T spaces the order becomes trivial and is of little interest.
Academic specialization As the volume of knowledge accumulated by humanity became too great, increasing specialization in academia appeared in response.
TensorFlow In May 2016 Google announced its tensor processing unit (TPU), a custom ASIC built specifically for machine learning and tailored for TensorFlow. The TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning.
Interactive specialization According to the second, the Interactive Specialization (IS)
Specialization (pre)order The specialization order is often considered in applications in computer science, where T spaces occur in denotational semantics. The specialization order is also important for identifying suitable topologies on partially ordered sets, as it is done in order theory.
Academic specialization In academia, specialization (or specialisation) may be a course of study or major at an academic institution or may refer to the field that a specialist practices in.
Partial template specialization Partial template specialization is a particular form of class template specialization. Usually used in reference to the C++ programming language, it allows the programmer to specialize only some arguments of a class template, as opposed to explicit specialization, where all the template arguments are provided.
Specialization (functional) Specialization (or specialisation) is the separation of tasks within a system. In a multicellular creature, cells are specialized for functions such as bone construction or oxygen transport. In capitalist societies, individual workers specialize for functions such as building construction or gasoline transport. In both cases, specialization enables the accomplishment of otherwise unattainable goals. It also reduces the ability of individuals to survive outside of the system containing all of the specialized components.
Functional specialization (brain) Functional specialization suggests that different areas in the brain are specialized for different functions.
In Practice According to the 2008 Journal Citation Reports, the five journals that have cited "In Practice" most often are (in order of citation descending citation frequency) "The Veterinary Record", "Journal of Small Animal Practice", "Journal of Veterinary Internal Medicine", "Journal of the American Veterinary Medical Association", and "In Practice" itself. As of 2008 the five journals that have been cited the most frequently by articles published in "In Practice" are "The Veterinary Record", "Journal of Small Animal Practitioners", "Journal of Veterinary Internal Medicine", "Journal of the American Veterinary Medical Association", and "In Practice" itself. According to the "Journal Citation Reports", the journal has a 2013 impact factor of 0.181.
Specialization (linguistics) Specialization refers to the narrowing of choices that characterizes an emergent grammatical construction. The lexical meaning of a grammaticalizing feature decreases in scope, so that in time the feature conveys a generalized grammatical meaning.
Cognitive specialization Watson et al. provide support for a specific specialization in language-dependent humor. Its adaptive value has both extrinsic and intrinsic components: humor facilitates social bonding if shared extrinsically, and provides pleasure if enjoyed in one's own mind. In addition, Johnson-Frey (2003) proposed a unique human specialization for tool use. According to Johnson-Frey, humans' ability to use tools is based on complex cognitive mechanisms, not just advanced sensorimotor skills. Rather than it being considered a purely physical specialization based only in motor areas of the brain, Johnson-Frey argues that tool use should be classified as a cognitive phenomenon due to its foundation in cognition. On a more philosophical level, Boyer (2003) argues that "religious thought and behavior" is a specialization that originally developed as a by-product of brain function, and its adaptive purposes led to its continued evolution by natural selection. Krueger et al. (2007) have argued that trust, which may form the foundation for helping and altruism and thus the basis of human social interaction, is also a cognitive specialization.