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If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

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This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Deep Neural Network for Beginners Using Python

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Deep Neural Network for Beginners Using Python

Dozent: Packt - Course Instructors

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5 Module

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In Ihrem eigenen Lerntempo lernen

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

8 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Understand the basics of training a DNN using the Gradient Descent algorithm.
Apply knowledge to implement a complete DNN using NumPy.
Analyze and create a complete structure for DNN from scratch using Python.
Evaluate and work on a project using deep learning for the IRIS dataset.

Kompetenzen, die Sie erwerben

Kategorie: Model Optimization
Kategorie: Network Architecture
Kategorie: Data Processing
Kategorie: Logistic Regression
Kategorie: Linear Algebra
Kategorie: Model Training
Kategorie: Artificial Neural Networks
Kategorie: Deep Learning
Kategorie: Applied Machine Learning
Kategorie: Data Preprocessing
Kategorie: Program Development
Kategorie: Machine Learning Algorithms

Werkzeuge, die Sie lernen werden

Kategorie: Pandas (Python Package)
Kategorie: Python Programming
Kategorie: NumPy

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4 Aufgaben

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In diesem Kurs gibt es 5 Module

Updated in May 2025.

This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Are you ready to become a deep learning expert? This step-by-step course guides you from basic to advanced levels in deep learning using Python, the hottest language for machine learning. Each tutorial builds on previous knowledge and assigns tasks solved in the next video. You will: - Learn to train machines to predict like humans by mastering data preprocessing, general machine learning concepts, and deep neural networks (DNNs). - Cover the architecture of neural networks, the Gradient Descent algorithm, and implementing DNNs using NumPy and Python. - Understand DNN methodologies with real-world datasets, such as the IRIS dataset. Designed for those interested in data science or advancing their skills in DNNs, this course requires a background in deep learning and a basic understanding of Python and mathematics will be helpful. It’s clear and beginner-friendly, teaching theoretical concepts followed by practical implementation.

In this module, we will provide a brief overview of the course and introduce the instructor. We will also outline the learning objectives and what students can expect to achieve by the end of the course.

Das ist alles enthalten

3 Videos1 Lektüre

In this module, we will delve into the foundational aspects of deep learning. We will start by examining a real-world problem and progressively introduce key concepts such as perceptrons, linear equations, and error functions. This section includes hands-on coding exercises to solidify understanding.

Das ist alles enthalten

37 Videos

37 VideosInsgesamt 126 Minuten

Problem to Solve Part 12 Minuten
Problem to Solve Part 23 Minuten
Problem to Solve Part 32 Minuten
Linear Equation3 Minuten
Linear Equation Vectorized3 Minuten
3D Feature Space4 Minuten
N-Dimensional Space3 Minuten
Theory of Perceptron2 Minuten
Implementing Basic Perceptron6 Minuten
Logical Gates for Perceptrons3 Minuten
Perceptron Training Part 12 Minuten
Perceptron Training Part 24 Minuten
Learning Rate3 Minuten
Perceptron Training Part 34 Minuten
Perceptron Algorithm1 Minute
Coding Perceptron Algo (Data Reading and Visualization)6 Minuten
Coding Perceptron Algo (Perceptron Step)8 Minuten
Coding Perceptron Algo (Training Perceptron)7 Minuten
Coding Perceptron Algo (Visualizing the Results)4 Minuten
Problem with Linear Solutions3 Minuten
Solution to Problem1 Minute
Error Functions2 Minuten
Discrete Versus Continuous Error Function3 Minuten
Sigmoid Function3 Minuten
Multi-Class Problem1 Minute
Problem of Negative Scores3 Minuten
Need of SoftMax2 Minuten
Coding SoftMax4 Minuten
One-Hot Encoding3 Minuten
Maximum Likelihood Part 16 Minuten
Maximum Likelihood Part 24 Minuten
Cross Entropy4 Minuten
Cross Entropy Formulation8 Minuten
Multi-Class Cross Entropy4 Minuten
Cross Entropy Implementation4 Minuten
Sigmoid Function Implementation1 Minute
Output Function Implementation2 Minuten

In this module, we will focus on more advanced topics in deep learning. We will cover gradient descent, logistic regression, and the architecture of neural networks. Practical coding sessions will help learners apply these concepts and build their own deep learning models.

Das ist alles enthalten

31 Videos1 Aufgabe

31 VideosInsgesamt 154 Minuten

Introduction to Gradient Descent5 Minuten
Convex Functions3 Minuten
Use of Derivatives3 Minuten
How Gradient Descent Works4 Minuten
Gradient Step2 Minuten
Logistic Regression Algorithm2 Minuten
Data Visualization and Reading6 Minuten
Updating Weights in Python4 Minuten
Implementing Logistic Regression13 Minuten
Visualization and Results9 Minuten
Gradient Descent Versus Perceptron5 Minuten
Linear to Non-Linear Boundaries5 Minuten
Combining Probabilities2 Minuten
Weighted Sums3 Minuten
Neural Network Architecture12 Minuten
Layers and DEEP Networks5 Minuten
Multi-Class Classification3 Minuten
Basics of Feed Forward8 Minuten
Feed Forward for DEEP Net5 Minuten
Deep Learning Algo Overview2 Minuten
Basics of Backpropagation7 Minuten
Updating Weights3 Minuten
Chain Rule for Backpropagation6 Minuten
Sigma Prime2 Minuten
Data Analysis NN (Neural Networks) Implementation6 Minuten
One-Hot Encoding (NN Implementation)3 Minuten
Scaling the Data (NN Implementation)2 Minuten
Splitting the Data (NN Implementation)5 Minuten
Helper Functions (NN Implementation)2 Minuten
Training (NN Implementation)13 Minuten
Testing (NN Implementation)3 Minuten

1 AufgabeInsgesamt 15 Minuten

Assessment 115 Minuten

In this module, we will address optimization challenges in deep learning. Topics include underfitting vs. overfitting, regularization techniques, and strategies to overcome common issues like local minima and vanishing gradients. Learners will gain insights into improving their model's performance and reliability.

Das ist alles enthalten

10 Videos

In this module, we will undertake a comprehensive final project, applying all the concepts and skills learned throughout the course. Starting with data exploration and progressing through model training and testing, this project will solidify your understanding and ability to implement deep learning solutions.

Das ist alles enthalten

5 Videos3 Aufgaben

Dozent

Packt - Course Instructors

Packt

1.926 Kurse565.050 Lernende

von

Packt

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Häufig gestellte Fragen

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.