TensorFlow in Practice Specialization by Instructor Laurence Moroney offered by deeplearning.ai on Coursera
I would like to thank Laurence Moroney for giving us wonderful exposure to discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.
This repository contains the solution of all the weekly quizzes and programming assignments of all four courses in TensorFlow in Practice specialization offered by deeplearning.ai on Coursera.
The first course is: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning.
After completing this first course in the specialization, now I
- Know best practices for using TensorFlow, a popular open-source machine learning framework
- Can build a basic neural network in TensorFlow
- Can train a neural network for a computer vision application
- Understand how to use convolutions to improve your neural network.
The second course is: Convolutional Neural Networks in TensorFlow. After completing this second course in the specialization, now I can
- Handle real-world image data
- Plot loss and accuracy
- Explore strategies to prevent overfitting, including augmentation and dropout
- Do transfer learning and how learned features can be extracted from models.
The third course is: Natural Language Processing in TensorFlow. After completing this third course in the specialization, now I can
- Build natural language processing systems using TensorFlow
- Process text, including tokenization and representing sentences as vectors
- Apply RNNs, GRUs, and LSTMs in TensorFlow
- Train LSTMs on existing text to create original poetry and more.
The fourth course is: Sequences, Time Series and Prediction. After completing this fourth and final course in the specialization, now I can
- Solve time series and forecasting problems in TensorFlow
- Prepare data for time series learning using best practices
- Explore how RNNs and ConvNets can be used for predictions
- Build a sunspot prediction model using real-world data.