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

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 learning models in NumPy, a powerful Python library and Keras, a cutting-edge library for deep learning in Python. You can test your knowledge with the quizzes that are provided at the end of every lesson and coding challenges that will help you gain a higher understanding. By the end of the course, you should have a general understanding of the basics in deep learning and you will be equipped with the right tools to learn more advanced concepts.

A Beginner's Guide to Deep Learning

About this Course

Learning Outcomes

✨Before We Begin

Fundamentals of Machine Learning

Machine Learning Paradigms

What is Deep Learning?

Programming Paradigm

✨Introduction to Deep Learning

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

✨Simple Perceptron Models 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

✨Towards Deep Neural Networks in NumPY

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

🖥️ Project: Build a Letter Classification Model

Deep Learning in NumPy Exam

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

✨Building Deep Learning Models with Keras

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

✨Fine-tune Keras Model

Introduction to MNIST Dataset

🍀 Challenge: Building the Network

Solution Review: Building the Network

Make Predictions

🖥️ Project: Build a Digit Recognition Model

🏆 A Quick Overview

🚀 What's Next?

✨Conclusion

Deep Learning in Keras Exam

A Beginner's Guide to Deep Learning

Join 450,000 learners from companies like

Teams of every size choose
Educative for Business
to train, upskill & onboard their developers

A Beginner's Guide to Deep Learning

Join 450,000 learners from companies like

Teams of every size choose Educative for Business to train, upskill & onboard their developers

Teams of every size choose
Educative for Business
to train, upskill & onboard their developers