•Introduction to Neural Networks and Deep Learning
•Course Overview
•Setting up your Environment
•Lesson Overview
•What are Neural Networks and Deep Learning
•Limitations of Deep Learning
•Common Components and Operations of Neural Networks
•Configuring a Deep Learning Environment
•Installing Python
•Installing TensorFlow Keras and TensorBoard
•Installing Jupyter Notebooks Pandas and NumPy
•Installation Completion
•Training a Neural Network with TensorFlow convolutional layer
•Training a Neural Network with TensorFlow fully connected layer
•Train a Neural Network with TensorFlow
•Testing network performance with unseen data
•Test your knowledge
•Model Architecture
•Choosing the Right Model Architecture
•Data Normalization
•Using Keras as a TensorFlow Interface
•Designing a Model
•Training a Model
•Making Predictions
•The Keras Paradigm
•From Data Preparation to Modeling
•Reshaping the TimeSeries Data
•Overfitting
•Model Evaluation and Optimization
•Model Evaluation
•Using TensorBoard
•Implementing Model Evaluation Metrics
•Evaluating Bitcoin Model
•Model Predictions
•Interpreting Predictions
•Hyperparameter Optimization
•Epochs Implementation
•Regularization Strategies Implementation
•Productization
•Handling and Dealing with New Data
•ReTraining an Old Model
•Training a New Model
•Deploying a Model as a Web Application
•Building and executing a Docker run command
•Deployment and using Cryptonic
•