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A Beginner's Guide to Deep Learning

Name: A Beginner's Guide to Deep Learning
Author: Educative

Estimated completion time: 20h

Certificate Included

course by:

Educative

Lessons

Quizzes

Challenges

Playgrounds

Code Snippets

214

Illustrations

Course Overview

This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies used in deep learning. In addition, this course will help you understand the importance of deep learning techniques. You will ex...

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Contents
Learning Experience
Course Contents
1. ✨Before We Begin
About this Course
Learning Outcomes
2. ✨Introduction to Deep Learning
Fundamentals of Machine Learning
Machine Learning Paradigms
What is Deep Learning?
Programming Paradigm
3. ✨Simple Perceptron Models in NumPY
Introduction to Perceptron
Coding the Perceptron Forward Propagation
Discrete vs. Continuous Prediction
🍀 Challenge: Use the Sigmoid Activation Function
Solution Review: Use the Sigmoid Activation Function
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
🍀 Challenge: Classification Using IRIS DataSet
Solution Review: Classification Using IRIS DataSet
🔩 Problems with Gradient Descent and the Fix
4. ✨Towards Deep Neural Networks in NumPY
Introduction to Non-Linear Boundaries
Neural Network
Feed Forward Propagation
Back Propagation
🍀 Challenge: Train the XOR Multilayer Perceptron
Solution Review: Train the XOR Multilayer Perceptron
Activation Functions
Deep Neural Network
5. 🖥️ Project: Build a Letter Classification Model
Introduction to the Letter Classification Data Set
🍀 Challenge: Forward Propagation - 3 Layered Neural Network
Solution Review: Forward Propagation - 3 Layered Neural Network
🍀 Challenge: Backpropagation - 3 Layered Neural Network
Solution Review: Backpropagation - 3 Layered Neural Network
🍀 Challenge: Training - 3 Layered Neural Network
Solution Review: Training - 3 Layered Neural Network
Making Predictions
Assessment 1
Deep Learning in NumPy Exam
6. ✨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
🍀 Challenge: Mine vs. Rock Classifier
Solution Review: Mine vs. Rock Classifier
7. ✨Fine-tune Keras Model
Introduction to Model Optimization
🍀 Challenge: Change the Model Optimizer
Solution Review: Change the Model Optimizer
Model Validation - I
Model Validation - II
Model Capacity
🍀 Challenge: Hypertune Model Parameters
Solution Review: Hypertune Model Parameters
8. 🖥️ Project: Build a Digit Recognition Model
Introduction to MNIST Dataset
🍀 Challenge: Building the Network
Solution Review: Building the Network
Make Predictions
9. ✨Conclusion
🏆 A Quick Overview
🚀 What's Next?
Assessment 2
Deep Learning in Keras Exam
How you will learn
Faster than videos
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Hands-on coding environments
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No set-up required
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Progress you can show
Built in assessments let you test your skills. Completion certificates let you show them off.