- RNNs
- Computer Vision
- Convolutional Neural Network
- Forecasting
- Transfer Learning
- Time Series
- Machine Learning
- Tokenization
- Dropouts
- Natural Language Processing
- TensorFlow
- Augmentation
DeepLearning.AI TensorFlow Developer
Completed by Nanhee Kim
January 10, 2021
Approximately 2 months at 10 hours a week to completeNanhee Kim's account is verified. Coursera certifies their successful completion of DeepLearning.AI DeepLearning.AI TensorFlow Developer Specialization.
Course Certificates Completed
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Convolutional Neural Networks in TensorFlow
Natural Language Processing in TensorFlow
Sequences, Time Series and Prediction
What you will learn
Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for computer vision applications.
Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout.
Build natural language processing systems using TensorFlow.
Apply RNNs, GRUs, and LSTMs as you train them using text repositories.
Skills you will gain
Course Certificates
Earned after completing each course in the Specialization
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
DeepLearning.AI
Taught by: Laurence Moroney
Completed by: Nanhee Kim by January 5, 2021
4 weeks, 4-5 hours/week
Convolutional Neural Networks in TensorFlow
DeepLearning.AI
Taught by: Laurence Moroney
Completed by: Nanhee Kim by January 10, 2021
4 weeks of study, 4-5 hours/week
Natural Language Processing in TensorFlow
DeepLearning.AI
Taught by: Laurence Moroney
Completed by: Nanhee Kim by January 10, 2021
4 weeks of study, 4-5 hours/week
Sequences, Time Series and Prediction
DeepLearning.AI
Taught by: Laurence Moroney
Completed by: Nanhee Kim by January 10, 2021
4 weeks of study, 4-5 hours/week