In this course, you will learn:
- Explain the fundamental ideas of TensorFlow, such as the major functions, operations, and execution pipelines.
- Explain how TensorFlow may be used to fit curves, perform regression, classify data, and minimise error functions.
- Learn about Convolutional Networks, Recurrent Networks, and Autoencoders, as well as other forms of Deep Architectures.
- While the Neural Networks are being trained, use TensorFlow for backpropagation to fine-tune the weights and biases.
1. Introduction to TensorFlow
- HelloWorld with TensorFlow
- Linear Regression
- Nonlinear Regression
- Logistic Regression
2. Convolutional Neural Networks (CNN)
- CNN Application
- Understanding CNNs
3. Recurrent Neural Networks (RNN)
- Intro to RNN Model
- Long Short-Term Memory (LSTM)
4. Restricted Boltzmann Machine
- Restricted Boltzmann Machine
- Collaborative Filtering with RBM
- Introduction to Autoencoders and Applications
- Deep Belief Network