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TensorFlow

The TensorFlow Fundamentals course is a program designed to provide participants with a deep understanding of TensorFlow, an open-source machine learning library developed by Google. Covering core principles, models, and practical applications, this course equips participants to build and deploy machine learning models using TensorFlow.

Explore the TensorFlow Fundamentals course—a comprehensive program delving into the core principles of TensorFlow, Google’s open-source machine learning library. Covering models and practical applications, this course equips participants to confidently build and deploy machine learning models using TensorFlow.”


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What you will learn

By the end of this course, participants will be able to:
  • Understand the fundamentals of TensorFlow and its applications
  • Build and execute TensorFlow graphs for machine learning tasks
  • Work with variables, constants, and operations in TensorFlow
  • Train neural networks, CNNs, and RNNs using TensorFlow
  • Deploy TensorFlow models for production using TensorFlow Serving
  • Explore advanced topics such as custom loss functions and TFX

Beneficial for

This course is suitable for:
  • Data Scientists
  • Machine Learning Engineers
  • Software Developers interested in TensorFlow

Course Pre-requisite

Participants should have a basic understanding of:
  • Basic understanding of machine learning concepts
  • Familiarity with programming (preferably Python)
  • Enthusiasm for building and deploying machine learning models using TensorFlow is key.

Course Outline

Understanding the fundamentals of TensorFlow

Overview of machine learning and deep learning with TensorFlow

Use cases and applications of TensorFlow in various domains

Installation and setup of TensorFlow

Building and executing a simple TensorFlow graph

Introduction to tensors and operations in TensorFlow

Declaring and using constants in TensorFlow

Working with variables for trainable model parameters

Initialization and management of TensorFlow variables

Understanding TensorFlow computation graphs

Creating and managing TensorFlow sessions

Graph optimization and visualization with TensorBoard

Performing mathematical operations with TensorFlow

Implementing optimization algorithms for model training

Customizing and using different optimizers in TensorFlow

Building and training neural networks with TensorFlow

Activation functions and their role in neural networks

Designing and implementing deep learning models in TensorFlow

Understanding convolutional layers in TensorFlow

Building and training CNNs for image recognition tasks

Transfer learning with pre-trained CNNs in TensorFlow

Introduction to recurrent layers in TensorFlow

Building and training RNNs for sequential data

Applications of RNNs in natural language processing and time-series analysis

Deploying TensorFlow models for production using TensorFlow Serving

Integration of TensorFlow models with web applications

Model deployment best practices and considerations

Implementing custom loss functions and metrics in TensorFlow

Handling data input pipelines with TensorFlow Dataset API

Exploring TensorFlow Extended (TFX) for end-to-end ML workflows

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