Getting Started with Tensorflow 20 faq

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instructor Instructor: Janani Ravi instructor-icon
duration Duration: 4.00 duration-icon

This course provides an introduction to TensorFlow 2.0, exploring its features and functionality for building and training neural networks. It covers the differences between TensorFlow 1.x and 2.0, as well as how the Keras high-level API and eager execution make TensorFlow 2.0 easy to use for complex models.

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Course Overview

❗The content presented here is sourced directly from Pluralsight platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

Getting Started with Tensorflow 20 is a course that focuses on introducing the TensorFlow 2.0 framework and exploring the features and functionality it offers for building and training neural networks. Participants will learn how TensorFlow 2.0 differs from TensorFlow 1.x and how the use of the Keras high-level API and eager execution makes TensorFlow 2.0 a very easy to work with even for complex models. The course will cover topics such as static and dynamic computation graphs, gradient descent optimization, and the different APIs in Keras. By the end of the course, participants will have the skills and knowledge to harness the computational power of the TensorFlow 2.0 framework and choose between the different model-building strategies available in Keras.

[Applications]
After completing this course, learners will be able to apply the knowledge they have gained to build and train neural networks using the TensorFlow 2.0 framework. They will be able to use the Keras high-level API and eager execution to quickly prototype and debug models. Learners will also be able to compare and contrast static and dynamic computation graphs and understand the advantages and disadvantages of working with each kind of graph. Additionally, learners will be able to use the tf.function decorator to decorate ordinary Python functions and harness the performance efficiencies of static graphs. Finally, learners will be able to use the different APIs in Keras to build regression and classification models.

[Career Paths]
Recommended Career Paths:
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use TensorFlow to build, train, and deploy models for various applications. They must have a strong understanding of the underlying algorithms and be able to optimize them for the best performance. The demand for Machine Learning Engineers is growing rapidly as more companies are looking to leverage the power of machine learning.

2. Data Scientist: Data Scientists use TensorFlow to analyze large datasets and uncover insights. They must have a strong understanding of the underlying algorithms and be able to optimize them for the best performance. Data Scientists must also be able to communicate their findings to stakeholders and make decisions based on their analysis.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers use TensorFlow to build and deploy AI applications. They must have a strong understanding of the underlying algorithms and be able to optimize them for the best performance. AI Engineers must also be able to communicate their findings to stakeholders and make decisions based on their analysis.

Developing Trends:
1. Automated Machine Learning: Automated Machine Learning (AutoML) is a rapidly growing field that uses TensorFlow to automate the process of building and training machine learning models. AutoML tools allow developers to quickly build and deploy models with minimal effort.

2. Deep Learning: Deep Learning is a subset of machine learning that uses TensorFlow to build and train neural networks. Deep Learning models are used for a variety of tasks such as image recognition, natural language processing, and autonomous driving.

3. Edge Computing: Edge Computing is a rapidly growing field that uses TensorFlow to deploy machine learning models on edge devices such as smartphones and IoT devices. Edge Computing allows developers to deploy models on devices with limited resources and still get the same performance as if they were running on a powerful server.

[Education Paths]
Recommended Degree Paths:
1. Bachelor of Science in Computer Science: This degree path provides a comprehensive overview of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and deep learning. This degree path is ideal for those looking to develop a strong foundation in computer science and use it to build powerful machine learning models with TensorFlow.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence and machine learning algorithms and their applications. It covers topics such as natural language processing, computer vision, and deep learning. This degree path is ideal for those looking to specialize in the development of AI and ML models with TensorFlow.

3. Master of Science in Data Science: This degree path focuses on the development of data science techniques and their applications. It covers topics such as data mining, data visualization, and predictive analytics. This degree path is ideal for those looking to specialize in the development of data-driven models with TensorFlow.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms and their applications. It covers topics such as reinforcement learning, generative models, and deep learning. This degree path is ideal for those looking to specialize in the development of complex machine learning models with TensorFlow.

Developing Trends:
1. Automated Machine Learning (AutoML): Automated machine learning is a rapidly growing field that focuses on automating the process of building machine learning models. AutoML tools allow users to quickly and easily build powerful models with minimal effort.

2. Distributed Computing: Distributed computing is a rapidly growing field that focuses on leveraging the power of multiple computers to speed up the training of machine learning models. Distributed computing allows users to train models faster and with more data than ever before.

3. Edge Computing: Edge computing is a rapidly growing field that focuses on leveraging the power of edge devices to run machine learning models. Edge computing allows users to run models on devices such as smartphones and IoT devices, allowing for real-time predictions and decisions.

4. Explainable AI: Explainable AI is a rapidly growing field that focuses on making machine learning models more interpretable and understandable. Explainable AI tools allow users to understand why a model made a certain prediction or decision, allowing for more trust in the model.

Course Provider

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AZ Class hope that this free trial Pluralsight course can help your Tensorflow skills no matter in career or in further education. Even if you are only slightly interested, you can take Getting Started with Tensorflow 20 course with confidence!

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