TensorFlow 2: Working with Neural Networks
43mIntermediate2022-01-31
Authors
Jonathan Fernandes
Consultant focusing on data science, AI, and big data
Course details
TensorFlow 2.0 is quickly becoming one of the most popular deep learning frameworks and a must-have skill in your artificial intelligence toolkit. Using a hands-on approach, instructor Jonathan Fernandes covers foundational skills for deep learning using TensorFlow 2.0, from creating single and multi-layer networks, to training a network, and using it to make predictions. He also covers loss functions, optimizers, and some of the data APIs unique to TensorFlow.
Skills covered
TensorFlowNeural Networks and Deep LearningGoogleArtificial Intelligence (AI)Deep Dive (X:Y)
Concepts
Introduction
- Using TensorFlow for neural networks and tables
- What you should know
- What is TensorFlow
Fashion MNIST and Neural Networks
- Working with the Fashion-MNIST dataset
- Neural network intuition
- Train the network
Working with Loss, Gradient Descent, and Optimizers
- Loss
- Gradient descent
- Optimizers
- Neural network visualization
Working with Tabular Data
- Introduction to the Titanic dataset
- Comparing the Titanic model to the Fashion-MNIST model
- tf.data API
- Feature engineering
- Train and evaluate the model
Conclusion
- Next steps and additional resources