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
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