A Beginner's Guide to Deep Learning

Gain insights into deep learning fundamentals, explore perceptron and advanced models, and discover practical coding in NumPy and Keras. Test your knowledge with quizzes and coding challenges.

Beginner

63 Lessons

20h

Certificate of Completion

Gain insights into deep learning fundamentals, explore perceptron and advanced models, and discover practical coding in NumPy and Keras. Test your knowledge with quizzes and coding challenges.

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Explanations

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Explanations

This course includes

2 Assessments

57 Playgrounds

13 Challenges

20 Quizzes

This course includes

2 Assessments

57 Playgrounds

13 Challenges

20 Quizzes

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

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

✨Before We Begin

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

About this Course

Learning Outcomes

✨Introduction to Deep Learning

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

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

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

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.

✨Fine-tune Keras Model

8 Lessons

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

Anomaly Detection in Medical Images: Python, TF, and PyTorch

Project

🖥️ Project: Build a Digit Recognition Model

4 Lessons

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

✨Conclusion

2 Lessons

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

Deep Learning in Keras Exam

Assessment

Purchase Price Prediction for Used Cars Using Deep Learning

Project

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