A Beginner's Guide to Deep Learning

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 dee... See More

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How You'll Learn

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Course Contents

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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