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Instructor-led Tensorflow Courses
Customized, role-based, expert-led Tensorflow Training
DevelopIntelligence specializes in delivering highly-customized, dedicated, role-based Tensorflow training courses to technical teams and organizations.
Of course, if you can't find the Tensorflow training course you're looking for, give us a call or contact us and we'll design one just for you and your team.
Our Tensorflow training offerings include:
Tensorflow Corporate Bootcamps
Tensorflow UpSkilling and ReSkilling Programs
Tensorflow New Hire Development Programs
Learning Strategies for Custom Tensorflow Projects
TensorFlow is a deep learning library built for numerical computation using data flow graphs. These graphs use the neural networks of deep learning to help you express and analyze patterns in large datasets.
Before understanding this deep learning library, it’s important to first grasp tensors and graphs. Put simply, TensorFlow creates graphs that represent mathematical operations. It also has tensors that represent multidimensional data arrays. The graph edges, then, show how these tensors communicate with each other.
Tensorflow is especially useful for deep learning but claims to be general enough to have a wide variety of different applications as well. Tensorflow is used by a number of different companies, including Dropbox, eBay, and Snapchat.
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Chat with one of our tech experts to create a custom on-site or online training program.
Learn Deep Learning concepts and popular tools.
Learn to build systems on Google Cloud to store and process batch or streaming data.
Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.
Learn how to use data engineering on Google Cloud to build specialized data pipelines for large scale streaming data analytics, machine learning, and recommendation systems.
Learn how to implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students’ understanding of underlying mathematical principles.
Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.