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Deep Learning with Keras Table of Contents


Table of Content
 


Introduction to Machine Learning with Keras

  • Course Overview
  • Installation and Setup
  • Lesson Overview
  • Data Representation
  • Loading a Dataset from the UCI Machine Learning Repository
  • Data Pre-Processing
  • Cleaning the Data
  • Appropriate Representation of the Data
  • Lifecycle of Model Creation
  • Machine Learning Libraries and scikit-learn
  • Keras
  • Model Training
  • Creating a Simple ModelModel TuningRegularization

Machine Learning versus Deep Learning

  • Lesson Overview
  • Introduction to ANNs
  • Linear Transformations
  • Matrix Transposition
  • Introduction to Keras

Deep Learning with Keras

  • Lesson Overview
  • Building Your First Neural Network
  • Gradient Descent for Learning the Parameters
  • Model Evaluation

Evaluate Your Model with Cross-Validation using Keras Wrappers

  • Lesson Overview
  • Cross-Validation
  • Cross-Validation for Deep Learning Models
  • Evaluate Deep Neural Networks with Cross-Validation
  • Model Selection with Cross-validation
  • Write User-Defined Functions to Implement Deep Learning Models with Cross-Validation

Improving Model Accuracy

  • Lesson Overview
  • Regularization
  • L1 and L2 Regularization
  • Dropout Regularization
  • Other Regularization Methods
  • Data Augmentation
  • Hyperparameter Tuning with scikit-learn

Model Evaluation

  • Lesson Overview
  • Accuracy
  • Imbalanced Datasets
  • Confusion Matrix
  • Computing Accuracy and Null Accuracy with Healthcare Data
  • Calculate the ROC and AUC Curves

Computer Vision with Convolutional Neural Networks

  • Lesson Overview
  • Computer Vision
  • Architecture of a CNN
  • Image Augmentation
  • Amending Our Model by Reverting to the Sigmoid Activation Function
  • Changing the Optimizer from Adam to SGD
  • Classifying a New Image

Transfer Learning and Pre-trained Models

  • Lesson Overview
  • Pre-Trained Sets and Transfer Learning
  • Fine Tuning a Pre-Trained Network
  • Classification of Images that are not Present in the ImageNet Database
  • Fine-Tune the VGG16 Model
  • Image Classification with ResNet

Sequential Modeling with Recurrent Neural Networks

  • Lesson Overview
  • Sequential Memory and Sequential Modeling
  • Long Short-Term Memory – LSTM
  • Predict the Trend of Apple's Stock Price Using an LSTM with 50 Units (Neurons)
  • Predicting the Trend of Apple's Stock Price Using an LSTM with 100 Units


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