Tensorflow | Part 2 | iNeuron

Welcome to Part 2 of our TensorFlow series !

In this session, we continue our exploration of TensorFlow, focusing on building and training neural networks. This session is designed for both beginners and intermediate users looking to deepen their understanding of TensorFlow and neural network concepts.

🔍 In this session, we cover:

- Review of TensorFlow Basics: Quick recap of key TensorFlow concepts from Part 1, including tensors, operations, and computational graphs.

- Building Neural Networks: Step-by-step guide to creating neural network architectures using TensorFlow's Keras API.

- Data Preprocessing: Techniques for preparing your data for training, including normalization, augmentation, and splitting datasets.

- Training Models: Learn how to compile and train your neural networks, specifying loss functions, optimizers, and metrics.

- Evaluating Model Performance: Methods to evaluate your model's performance using validation data and metrics such as accuracy, precision, and recall.

-Hyperparameter Tuning: Explore strategies for tuning hyperparameters to optimize your model's performance.

Saving and Loading Models: Best practices for saving your trained models and loading them for inference or further training.

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Видео Tensorflow | Part 2 | iNeuron канала iNeuron Intelligence