Learn TensorFlow and practice your ML skills with our comprehensive learning path. Ideal for beginner and intermediate ML Engineers, it covers the essentials and advanced techniques for building and optimizing Deep Learning models.
Overview
Syllabus
- Introduction to TensorFlow Basics
- Dive into TensorFlow, one of the top libraries for numerical computation and AI. This beginner-friendly course focuses on TensorFlow's core: tensors and their use in neural networks. Learn about tensors, tensor operations, and basic TensorFlow components to begin creating simple neural network models.
- Building a Neural Network in TensorFlow
- Dive into TensorFlow to learn and build basic neural networks. This course covers key elements like layers, neurons, activation functions, and model training. Progress through hands-on code examples, ending with building and assessing a neural network for binary classification.
- Modeling the Iris Dataset with TensorFlow
- Explore the famous Iris dataset in our advanced TensorFlow course. Learn to preprocess data, build, and train a multi-class classifier. Evaluate performance with metrics and visualizations. Conclude with model optimization techniques to boost efficiency and accuracy, and cover saving/loading for deployment.
- TensorFlow Techniques for Model Optimization
- This course delves into advanced TensorFlow techniques to boost model performance and reliability. Learn about using regularization and dropout to prevent overfitting, and explore real-time training improvements with callbacks. Each module is concise and impactful, equipping you with practical skills to enhance your machine learning models.
Courses
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Dive into TensorFlow, one of the top libraries for numerical computation and AI. This beginner-friendly course focuses on TensorFlow's core: tensors and their use in neural networks. Learn about tensors, tensor operations, and basic TensorFlow components to begin creating simple neural network models.
-
Dive into TensorFlow to learn and build basic neural networks. This course covers key elements like layers, neurons, activation functions, and model training. Progress through hands-on code examples, ending with building and assessing a neural network for binary classification.
-
Explore the famous Iris dataset in our advanced TensorFlow course. Learn to preprocess data, build, and train a multi-class classifier. Evaluate performance with metrics and visualizations. Conclude with model optimization techniques to boost efficiency and accuracy, and cover saving/loading for deployment.
-
This course delves into advanced TensorFlow techniques to boost model performance and reliability. Learn about using regularization and dropout to prevent overfitting, and explore real-time training improvements with callbacks. Each module is concise and impactful, equipping you with practical skills to enhance your machine learning models.
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Introduction to TensorFlow Basics
