A Beginner's Guide to Deep Learning

Description

In this course, you will learn :

• Fundamental concepts and terminologies used in deep learning.
• Understand the significance of deep learning techniques.
• Simple models such as perceptrons before moving on to more complex but powerful deep learning models.
• How to code simple and complex deep learning models in NumPy, a powerful Python library, and Keras, a cutting-edge Python deep learning library.

Syllabus :

1. Introduction to Deep Learning

• Fundamentals of Machine Learning
• Machine Learning Paradigms
• What is Deep Learning?
• Programming Paradigm

 

2. Simple Perceptron Models in NumPY

• Introduction to Perceptron
• Coding the Perceptron Forward Propagation
• Discrete vs. Continuous Prediction
• The Error Function
• Challenge: Scaling Error Up to Multiple Data Points
• Solution Review: Scaling Error Upto Multiple Data Points
• Optimizing the Perceptron Output
• Gradient Descent: Stochastic vs. Batch Update
• Gradient Descent: The Stochastic Update
• Gradient Descent: The Batch Update
• Perceptrons as Logical Operators
• Problems with Gradient Descent and the Fix

 

3. Towards Deep Neural Networks in NumPY

• Introduction to Non-Linear Boundaries
• Neural Network
• Feed Forward Propagation
• Back Propagation
• Activation Functions
• Deep Neural Network

 

4. Building Deep Learning Models with Keras

• Introduction to Keras
• Keras Workflow
• Read and Explore the Data
• Specify Model Architecture
• Model Compilation
• Fitting a Model
• Model Evaluation
• Using Models
• Regression Models

 

5. Fine-tune Keras Model

• Introduction to Model Optimization
• Model Validation
• Model Capacity

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