Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

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What Will You Learn?

Learn best practices for using TensorFlow, a popular open-source machine learning framework

Build a basic neural network in TensorFlow

Train a neural network for a computer vision application

Understand how to use convolutions to improve your neural network

About This Course

Provider: Coursera

Format: Online

Duration: 18 hours to complete [Approx]

Target Audience: Intermediate

Learning Objectives: Upon completion this free course, you will be equipped with a solid understanding of the fundamental principles of Machine Learning and Deep Learning, and proficiently apply them using TensorFlow to build scalable models for real-world problem-solving.

Course Prerequisites: Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

Assessment and Certification: Earn a Certificate upon completion from the relevant Provider

Instructor: DeepLearning.AI

Key Topics: Computer Vision, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow

Topic Covered:

- A primer in machine learning
- The ‘Hello World’ of neural networks
- Working through ‘Hello World’ in TensorFlow and Python
- An Introduction to computer vision
- Writing code to load training data
- Coding a Computer Vision Neural Network
- Walk through a Notebook for computer vision
- Using Callbacks to control training
- Walk through a notebook with Callbacks
- What are convolutions and pooling?
- Implementing convolutional layers
- Implementing pooling layers
- Improving the Fashion classifier with convolutions
- Walking through convolutions
- Understanding ImageDataGenerator
- Defining a ConvNet to use complex images
- Training the ConvNet
- Walking through developing a ConvNet
- Walking through training the ConvNet
- Adding automatic validation to test accuracy
- Exploring the impact of compressing images

Course Details