Introduction to Tensorflow and Keras
Learn tensorflow & keras in this free online training. Tensorflow & keras course is taught hands-on by experts. Learn how to get started with tensorflow and convolution process in details. Best for beginners. Enroll for free now!
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About this Course
This Introduction to TensorFlow and Keras course will enrich your knowledge about all the essential Python libraries and guide you to install them step by step. It will start by explaining what TensorFlow is and its various functionalities. It will then take you to get a brief understanding of the prerequisites to understand tensors before moving on to the installation chapter. Once your concept of TensorFlow is clear, the course will have you navigate through Neural Networks and discuss how you can write Tensor code to perform distinct operations. Before wrapping up the course, you will also learn about the newer version (TensorFlow 2.x) and learn to perform Linear Regression using TensorFlow. In the last phase, the course will take you through the hands-on session, which will discuss two use cases and finally throw light on what Keras is, its features, character recognition, and image classification using CNN.
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Course Outline
With the high demand for Deep Learning, it is essential to learn about TensorFlow to create Deep Learning models. Here, we will learn what TensorFlow is, the essential library, and the in-demand skill.
This section will continue discussing the various functionalities of TensorFlow, including Google Colab, reasons to learn TensorFlow, and Languages & OS supported by TensorFlow.
This section will discuss the prerequisites to understand tensors, including Linear Algebra, Vector Calculus, and Python Calculus, and how they work together to provide effective results.
In this section, you will learn about the latest version and compatibility and how you can install TensorFlow based on what GUI/CLI you use for Python.
You will understand what Neural Networks are and how they are a pathway to Artificial Intelligence using Deep Learning. This section will also give a brief idea of weights & mathematics in Neural Networks.
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