TensorFlow Course with Hands-on Training

Objectives

• Gain a solid understanding of the TensorFlow framework, including its architecture, components, and workflow for building machine learning models.
• Learn how to install and set up TensorFlow for your development environment, ensuring a smooth and efficient working environment.
• Develop proficiency in TensorFlow's core concepts, such as tensors, operations, variables, and computational graphs, to effectively define and manipulate models.
• Understand how to build, train, and evaluate neural networks using TensorFlow, leveraging its high-level APIs and pre-built layers for tasks like image classification or natural language processing.
• Learn how to implement various popular deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), using TensorFlow.
• Gain knowledge of TensorFlow's data handling capabilities, including data preprocessing, augmentation, and loading data from various sources like CSV files, image datasets, or text corpora.
• Explore techniques for model optimization and regularization, such as dropout, batch normalization, and learning rate scheduling, to improve the performance and generalization of TensorFlow models.
• Learn how to deploy TensorFlow models in production environments, including techniques for model serving, model conversion, and integration with web applications or mobile devices.