DeepLearning.AI - Convolutional Neural Networks in TensorFlow
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Convolutional Neural Networks in TensorFlow at Coursera Overview
Duration | 26 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Convolutional Neural Networks in TensorFlow at Coursera Highlights
- Taught by top companies and universities.
- Affordable programs and 7 day free trial.
- Shareable Certificate upon completion.
Convolutional Neural Networks in TensorFlow at Coursera Course details
- If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
- In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer 'sees' information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.
- The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Convolutional Neural Networks in TensorFlow at Coursera Curriculum
Exploring a Larger Dataset
Introduction, A conversation with Andrew Ng
A conversation with Andrew Ng
Training with the cats vs. dogs dataset
Working through the notebook
Fixing through cropping
Visualizing the effect of the convolutions
Looking at accuracy and loss
Week 1 Wrap up
Before you Begin: TensorFlow 2.0 and this Course
The cats vs dogs dataset
Looking at the notebook
What you'll see next
What have we seen so far?
Week 1 Quiz
Augmentation: A technique to avoid overfitting
A conversation with Andrew Ng
Introducing augmentation
Coding augmentation with ImageDataGenerator
Demonstrating overfitting in cats vs. dogs
Adding augmentation to cats vs. dogs
Exploring augmentation with horses vs. humans
Week 2 Wrap up
Image Augmentation
Start Coding...
Looking at the notebook
The impact of augmentation on Cats vs. Dogs
Try it for yourself!
What have we seen so far?
Week 2 Quiz
Transfer Learning
A conversation with Andrew Ng
Understanding transfer learning: the concepts
Coding transfer learning from the inception mode
Coding your own model with transferred features
Exploring dropouts
Exploring Transfer Learning with Inception
Week 3 Wrap up
Start coding!
Adding your DNN
Using dropouts!
Applying Transfer Learning to Cats v Dogs
What have we seen so far?
Week 3 Quiz
Multiclass Classifications
A conversation with Andrew Ng
Moving from binary to multi-class classification
Explore multi-class with Rock Paper Scissors dataset
Train a classifier with Rock Paper Scissors
Test the Rock Paper Scissors classifier
A conversation with Andrew Ng
Introducing the Rock-Paper-Scissors dataset
Check out the code!
Try testing the classifier
What have we seen so far?
Wrap up
Week 4 Quiz
Convolutional Neural Networks in TensorFlow at Coursera Admission Process
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