This course covers the fundamentals of TensorFlow 2.0, a popular Python library for deep learning. By the end of the course, learners will be able to install TensorFlow, understand neural networks, build a convolutional neural network, and perform digit classification using the MNIST dataset. The teaching method includes theoretical explanations, hands-on sessions, and practical examples. This course is intended for individuals interested in deep learning, neural networks, and TensorFlow.
Overview
Syllabus
Introduction.
Agenda.
Introduction to TensorFlow.
What are Tensors?.
How to install TensorFlow?.
Introduction to Neural Networks.
Mathematics behind Neural Networks.
Getting started with TensorFlow.
Hands-on sessions using TensorFlow.
Digit classification using MNIST dataset.
How does a Convolutional Neural Network work?.
Binary image classifier with CNNs.
Summary.
Taught by
Great Learning
