Deep Learning with TensorFlow and Keras

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