Python Deep Learning

Part I

Module 1: The Foundations of Machine Learning

• Advantages of Machine Learning
• Disadvantages of Machine Learning
• Subjects Involved in Machine Learning
  • Statistics
  • Brain Modeling
  • Adaptive Control Theory
  • Psychological Modeling
  • Artificial Intelligence
  • Evolutionary Models
• Programming Languages
  • R
  • Python

Module 2: Machine Learning Models

• Supervised Machine Learning
• Unsupervised or Reinforcement Learning
• How to Evaluate Machine Learning Models

Module 3: An Introduction to Nueral Networks

• Historical Background
• Why Use Neural Networks?
• Neural Networks vs. Conventional Computers
• Types of Neural Networks
  • Fully Connected Neural Network
  • Feed-Forward Networks
  • Convolutional Neural Networks
  • Feedback Networks
  • Perceptrons
  • Recurrent Neural Networks
  • Generative Adversarial Network

Module 4: An Introduction to Deep Learning

• Supervised Modes
• Unsupervised Modes
• How to Obtain Impressive Results Using Deep Learning
• Examples of Deep Learning
• Automated Driving
• Defense and Aerospace
• Medical Research
• Industrial Automation
• Electronics
• How Does Deep Learning Work?
• Why Deep Learning is Better Than Traditional Learning Methods
• Choosing Between Deep Learning and Machine Learning

Module 5: How to Create and Train Deep Learning Models

• Training from Scratch
• Transfer Learning
• Feature Extraction

Module 6: Applications of Deep Learning

• Recognize applications of deep learning.

Module 7: Activation Functions Used to Develop Deep Learning Models

• Popular Activation Functions
• Choosing the Right Activation Function

Part II

Module 8: An Introduction to Python

• Running Python
• Installing on Windows
• Installing on Other Systems
• Choosing the Right Version
• Python Keywords
• Understanding the Naming Convention
• Creating and Assigning Values to Variables
• Recognizing Different Types of Variables
• Working with Dynamic Typing
• The None Variable
• Computers Only Take Zeros and ones
• Deep Learning Libraries in Python.

Module 9: How to Clean Data Using Python

• Dropping Columns in a Data Frame
• Changing the Index of a Data Frame
• Tidying up Fields in the Data
• Cleaning the Entire Data Set Using the applymap().

Module 10: How to Manipulate Data Using Python

• Starting with Numpy
  • Creating Arrays
  • Array Indexing
  • Array Slicing
  • Array Concatenation

Module 11: Python Environment for Deep Learning

• Installation of Keras, TensorFlow, and Theano

Modul 12: Regression Problem Using Keras

• Develop the Baseline Model
• Modeling a Standardized Data Set
• Tune the Network Typology
• Evaluate a Deeper Network Topology
• Evaluate a Wider Network Topology

Module 13: How to Develop a Neural Network in Python using Keras

• Load Data
• Define the Model
• Compile the Model
• Fit the Model
• Evaluate the Model
• Tie It All Together
• Make Predictions

Module 14: How to Evaluate the Performance of a Deep Learning Model

• Empirically Evaluate Network Configurations
• Data Splitting
• Manual K-Fold Cross Validation

Module 15: How to Save and Load Deep Learning Models

• Save Your Neural Network Model to JSON
• Save Your Neural Network Model to YAML

Module 16: Reducing Dropouts in Deep Learning Models

• Dropout Regularization for Neural Networks
• Regularizing Dropouts in Keras
  • Using Dropout on Visible Layers
  • Using Dropout on Hidden Layers
• Tips for Using Dropout