• Must have some python programming experience and NumPy. Students must also be familiar with Machine Learning Concepts

• Machine learning enthusiasts and practitioners who are familiar with basic ML concepts (e.g., models, datasets, training loops). Learners who want to start building deep learning models and need a solid foundation in TensorFlow tensors.

Course Outline:

1. Getting started with TensorFlow?
   • What is TensorFlow? Why TensorFlow for ML?
2. Understanding Tensors
   • What are Tensors? How are they different for NumPy Arrays?
   • Types of Tensors: constant, random, zeros, ones, eye
   • Tensor data types: float32, int32
   • Hands-on: Creating Tensors
3. Accessing and Manipulating Tensor Data
   • Indexing and Slicing Tensors
   • Reshaping Tensors
   • Adding and removing dimensions to Tensors
   • Hands-on: Working with Tensors
4. Mathematical Operations on Tensors
   • Element-wise operations: add, subtract, multiply, divide
   • Matrix operations: matrix multiplication and dot products
   • Aggregations: sum, mean, argmax, argmin
   • Hands-on: Mathematical operations on Tensors
5. Activation Functions on Tensors
   • Learn about different types of activation functions on Tensors
   • Apply activation functions: ReLU, Sigmoid, Tanh, etc
   • Visualize the output of the functions on Tensors
   • Hands-on: Modifying Tensors

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools