Course Description
TensorFlow platform has established itself as one of the most popular, powerful, and flexible frameworks for building, training, and deploying ML and DL models. With the release of TensorFlow 2, the framework underwent significant improvements and introduced new amazing features, making it easier for students to build and train models.
This course is structured to teach you how to build, train, validate, and save neural network models. You will gain hands-on experience by working on practical assignments to solidify your understanding and apply what you learn.
Whether you are just starting your journey in deep learning or looking to deepen your existing knowledge, this course will equip you with the skills needed to develop robust and efficient machine learning models using TensorFlow 2, making you well-prepared to tackle real-world challenges in the field of deep learning.
What you'll learn
After completing this course you will be able to :
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Build and train CNN models using TensorFlow2.
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Apply different weight initialization methods in CNNs.
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Compile TensorFlow2 models for efficient performance optimization.
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Implement model evaluation and prediction processes.
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Create and utilize validation datasets within your TensorFlow2 model training process.
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Employ various model regularization techniques to combat overfitting.
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Implement batch normalization to improve the efficiency and reliability of neural network models.
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Construct custom callbacks to track specific metrics.
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Apply built-in callbacks like checkpoints and early stopping.
Save and load TensorFlow2 models effectively based on your project requirements.
Requirements
Mahara-Tech