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Beginner

20h

A Beginner's Guide to Deep Learning
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Discover fundamental deep learning concepts, master coding models using NumPy and Keras, and test your knowledge with quizzes and coding challenges. Gain insights into effective deep learning techniques.

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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 examine simple models like perceptron before learning more complex yet powerful deep learning models. The course will provide hands-on practical knowledge of how to code simple and complex deep ...Show More

This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will ...Show More

TAKEAWAY SKILLS

Machine learning fundamentals

Machine learning paradigms

Deep learning basics

Perceptron

Gradient descent

Activation functions

Deep neural networks

Neural networks

Deep learning models in Keras

Fine-tuning models in Keras

Course Content

63 Lessons20 Quizzes2 Assessments13 Challenges

✨Before We Begin

2 Lessons

Get familiar with the basics of deep learning and practical Python coding skills.

About this Course

Learning Outcomes

✨Introduction to Deep Learning

4 Lessons

Grasp the fundamentals of machine learning paradigms, deep learning principles, and neural networks.

Fundamentals of Machine Learning

Machine Learning Paradigms

What is Deep Learning?

Programming Paradigm

✨Simple Perceptron Models in NumPY

16 Lessons

Work your way through perceptron models, coding, prediction methods, and optimization techniques using 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

✨Towards Deep Neural Networks in NumPY

8 Lessons

Break down the steps to foundational concepts of neural networks and their training processes.

Introduction to Non-Linear Boundaries

Neural Network

Feed Forward Propagation

BackPropagation

🍀 Challenge: Train the XOR Multilayer Perceptron

Solution Review: Train the XOR Multilayer Perceptron

Activation Functions

Deep Neural Network

🖥️ Project: Build a Letter Classification Model

8 Lessons

Solve problems in building and training a letter classification model with deep learning.

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

Deep Learning in NumPy Exam

Assessment

✨Building Deep Learning Models with Keras

11 Lessons

Tackle building, compiling, training, evaluating, and utilizing deep learning models using Keras.

Introduction to Keras

Keras Workflow

Read and Explore the Data

Specify Model Architecture

🍀 Challenge: Mine vs. Rock Classifier

Solution Review: Mine vs. Rock Classifier

✨Fine-tune Keras Model

8 Lessons

Master the steps to optimize, validate, and hypertune deep learning models for improved accuracy.

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

🖥️ Project: Build a Digit Recognition Model

4 Lessons

Try out building and evaluating a digit recognition model using the MNIST dataset.

Introduction to MNIST Dataset

🍀 Challenge: Building the Network

Solution Review: Building the Network

Make Predictions

10.

✨Conclusion

2 Lessons

Walk through the key points of ANNs, RNNs, and CNNs and their applications.

🏆 A Quick Overview

🚀 What's Next?

Deep Learning in Keras Exam

Assessment

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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