In the Deep Learning with TensorFlow and Keras training course, you'll learn:
Introducing Deep Learning as a Tool
- Advantages and Disadvantages
- Frameworks and Languages
- Introducing Tensorflow
- Introducing Keras
Developing a Linear Single-Layer Perceptron for Binary Classification and Regression
- Decision Boundaries (Linear / Non-Linear)
- Weights / Bias
- Gradient Descent Optimization and Backpropagation
- Loss Functions (SSE, Cross-Entropy)
- Activation Functions (sigmoid, tanh, relu, leaky relu)
- Performing Regression Output
Performing Multi-Class Classification
- Introducing the Softmax Function
- How to Use Softmax Function for Multi-class Classification
- Sizing Input and Output layers for Multi-class Classification
Multi-Layer Perceptron Models (MLP)
- Hidden Layers and Non-Linear Decision Boundaries
- How to Size Multiple Decision Layers
- Curse of Dimensionality and Limitation of Feedforward Networks
Using Image Data with MLP
- How to Classify Image Data using MLP
- Limitations of LMP for Image Data
- L How to Perform Image Attribution in Deep Learning
- Relating Images with Tags
- Identifying Elements within Images
Feature Transformations for Image Processing
- Using OpenCV to Perform Front-End Image Processing
- Edge Detection Processing
- Dimensionality Reduction
- Image Preprocessing
CNNs and Image Recognition
- Convolutional Layers and Max Pooling Layers
- How Convolutional Layers Help Solve the Curse of Dimensionality
- Using CNNs to Improve Image Classifier
- Lab: CNNs and Image Classification
Transfer Learning
- Using Pre-trained Models (inception_v3 and Imagenet5)
- Adapting Pre-Trained Models to New Situations by Adding Layers
Attribution
RNNs and LSTMs
- How RNNs allow us to Model Dynamic Systems
- Feedback Loops and Stability in RNNs
- LSTM Architectures and Memory Units
- Sequential Learning
