What programming languages and tools are used in this course?

The course uses Python along with TensorFlow, Keras, and supporting libraries like NumPy and Pandas.

Does the course cover neural network architectures?

Yes, you’ll learn perceptrons, multilayer perceptrons, convolutional neural networks, and more.

Can these skills help me in a data science or AI career?

Absolutely. Deep learning is a core skill for data science, AI engineering, and research roles across industries.

How is this course different from traditional machine learning courses?

This course focuses specifically on deep neural networks, feature learning, and large-scale AI applications.

Will I earn a certificate after completing the course?

Yes, you’ll receive a Coursera certificate to showcase your deep learning expertise to employers and on LinkedIn.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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.

Practical Deep Learning with Python

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Practical Deep Learning with Python

Dieser Kurs ist Teil von Spezialisierung „Mastering AI: Neural Nets, Vision System, Speech Recognition“

Dozent: Edureka

Bei enthalten

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4 Module

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unter 10 Stunden pro Woche

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In Ihrem eigenen Lerntempo lernen

4 Module

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

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Understand the core components of deep learning models and their role in AI.
Apply CNN, R-CNN, and Faster R-CNN for object detection tasks.
Implement RNNs and LSTMs for sequential data processing.
Optimize and evaluate deep learning models for improved performance.

Kompetenzen, die Sie erwerben

Kategorie: Applied Machine Learning
Kategorie: Fine-tuning
Kategorie: Convolutional Neural Networks
Kategorie: Recurrent Neural Networks (RNNs)
Kategorie: Machine Learning Methods
Kategorie: Artificial Intelligence
Kategorie: Model Training
Kategorie: Model Evaluation
Kategorie: Model Optimization

Werkzeuge, die Sie lernen werden

Kategorie: Python Programming

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In diesem Kurs gibt es 4 Module

Gain hands-on experience in deep learning with Python and learn to design, train, and optimize advanced neural networks for real-world artificial intelligence applications. This course is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to enhance their skills in building intelligent systems using Python.

Throughout this deep learning training, you’ll explore how to model and analyze complex datasets with techniques widely applied in computer vision, natural language processing, and predictive analytics. You’ll also develop the ability to solve large-scale data problems and uncover actionable insights through deep learning. By the end of the course, you will be able to: - Explain the foundational components of deep learning models and their significance in artificial intelligence. - Apply Convolutional Neural Networks (CNNs), R-CNNs, and Faster R-CNNs for object detection and image-related applications. - Recognize the limitations of Perceptrons and implement Multi-Layer Perceptrons (MLPs) for improved data modeling. - Build and apply Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures for sequential and time-series data. - Optimize, evaluate, and fine-tune neural networks to improve accuracy, efficiency, and scalability. This course is designed for professionals and learners with a working knowledge of Python and machine learning who are ready to expand into deep learning and artificial intelligence. Experience with Python programming, statistics, and prior machine learning projects will be helpful in making the most of this training. Begin your journey into deep learning with Python and strengthen your ability to build advanced AI systems that solve real-world problems and power the future of intelligent technologies.

In this module, you will explore the fundamental components of deep learning by designing perceptron and implementing their functionality. You will address the limitations of perceptron by utilizing Multi-Layer Perceptron (MLPs) and observe how MLPs significantly enhance model performance.

Das ist alles enthalten

25 Videos4 Lektüren4 Aufgaben2 Diskussionsthemen

25 VideosInsgesamt 113 Minuten

Course Introduction5 Minuten
Environment Configuration2 Minuten
Machine Learning vs. Deep Learning5 Minuten
What is Deep Learning?3 Minuten
Neural Networks6 Minuten
Artificial Neural Network (ANN)6 Minuten
ANN: Types and Applications4 Minuten
Forward Propagation4 Minuten
Perceptron7 Minuten
Learning Rate7 Minuten
What is Activation Function? 4 Minuten
Activation Function and it's Types5 Minuten
Importance of Epoch5 Minuten
Single Layer Perceptron - Define Sigmoid Function 6 Minuten
Single Layer Perceptron - Decision Boundary7 Minuten
Limitations of Single Layered Perceptron2 Minuten
Multi-Layered Perceptron2 Minuten
What is Backpropagation? 2 Minuten
Backpropagation 3 Minuten
Demonstration: Building a Simple Neural Network4 Minuten
Demonstration: Understanding How Backpropagation has Worked4 Minuten
Demonstration: Handwritten Digits Classification - Data Preprocessing 4 Minuten
Demonstration: Handwritten Digits Classification- Designing the Model5 Minuten
Demonstration: Handwritten Digits Classification - Optimizing the Model 5 Minuten
Summary of Deep Learning Components6 Minuten

4 LektürenInsgesamt 40 Minuten

Welcome to Practical Deep Learning with Python10 Minuten
System Requirements and Pre-requisite for Studying Deep Learning10 Minuten
Learning Rate in Deep Learning10 Minuten
Hebbian Learning Algorithm10 Minuten

4 AufgabenInsgesamt 48 Minuten

Practice Quiz : Environment Set-Up and Configuration6 Minuten
Practice Quiz : Essentials for Deep Learning6 Minuten
Practice Quiz : Building Perceptron and it's Working6 Minuten
Knowledge Check : Deep Learning Components30 Minuten

2 DiskussionsthemenInsgesamt 20 Minuten

Introduce Yourself10 Minuten
What are the structural and functional similarities between the human brain and neural networks?10 Minuten

In the second module of this course, learners will learn about the working of Convolutional Neural Networks (CNN) and understand their importance in training deep learning models. Learners will also work on improving CNN model performance using RCNN and Faster RCNN, observe the computation time of these models, and gauge their accuracy score.

Das ist alles enthalten

27 Videos3 Lektüren4 Aufgaben1 Diskussionsthema

27 VideosInsgesamt 126 Minuten

Limitations of MLP4 Minuten
MLP Limitations: Resolving the Issue with CNN3 Minuten
Visual Cortex and CNN7 Minuten
Convolutional Layer 6 Minuten
Working of Convolutional Layer 6 Minuten
Demonstration: Load and Preprocess the Data 5 Minuten
Demonstration: Designing the Model 5 Minuten
Demonstration: Building the CNN Model 3 Minuten
Demonstration: Model Accuracy 2 Minuten
Demonstration: Adding More Layers 5 Minuten
Demonstration: Building Basic CNN Model with New Parameters5 Minuten
Demonstration: Pre-trained Model 3 Minuten
Classification and Object Detection6 Minuten
Introduction to RCNN5 Minuten
R-CNN: Bounding Box Regression2 Minuten
Pre-trained Model6 Minuten
Fast Regional - CNN6 Minuten
Demonstration: Creating Base Variables and Loading the Model4 Minuten
Demonstration: Training the Model and Visualizing the Predictions4 Minuten
Demonstration: SVM as a Classifier3 Minuten
Fast RCNN Limitations5 Minuten
Advent of Faster R-CNN6 Minuten
Tensorflow Hub4 Minuten
Demonstration: Object Detection with Faster RCNN-Pretrained Model setup6 Minuten
Demonstration: Object Detection with Faster RCNN - Building the Model6 Minuten
Summary of CNN in Deep Learning3 Minuten
Summary of Faster RCNN4 Minuten

3 LektürenInsgesamt 30 Minuten

Why Convolutions are Important?10 Minuten
SVM Classifier in Object Detection 10 Minuten
Faster R-CNN Architecture10 Minuten

4 AufgabenInsgesamt 48 Minuten

Practice Quiz : CNN6 Minuten
Practice Quiz : TensorFlow Hub for Object Detection using Faster RCNN6 Minuten
Practice Quiz : Faster RCNN (Recurrent Convolutional Neural Network)6 Minuten
Knowledge Check : Deep Learning with CNN, RCNN and Faster RCNN30 Minuten

1 DiskussionsthemaInsgesamt 10 Minuten

Which among the following techniques is most useful?10 Minuten

This module focuses on Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data processing. Learners will gain practical skills in building, training, and optimizing models for complex tasks.

Das ist alles enthalten

24 Videos4 Lektüren4 Aufgaben

24 VideosInsgesamt 126 Minuten

RNN Fundamentals5 Minuten
RNN Architecture4 Minuten
RNN Architecture: Workflow5 Minuten
Implementing RNN7 Minuten
Demonstration: RNN-Dataset Preparation 6 Minuten
Demonstration: RNN-Building the Model 6 Minuten
Basics of LSTM6 Minuten
LSTM Structure6 Minuten
Forget Gate and Input Gate6 Minuten
Output Gate3 Minuten
Importance of LSTM Architecture5 Minuten
Types of LSTM4 Minuten
Demonstration: Next Word Prediction- Processing the Corpus6 Minuten
Demonstration: Next Word Prediction- Layers 5 Minuten
Demonstration: Next Word Prediction- Model Compilation and Prediction7 Minuten
Improving a Model6 Minuten
Model Optimization4 Minuten
Using Adam Optimizer7 Minuten
Model Compilation3 Minuten
Model Compilation with Popular Frameworks4 Minuten
Demonstration: Model Compilation- Preparing the Dataset5 Minuten
Demonstration: Building and Compiling Model 5 Minuten
Demonstration: From RMSProp to Adam 4 Minuten
Summary of Deep Learning with RNN and LSTM with Model Optimization5 Minuten

4 LektürenInsgesamt 40 Minuten

Recurrent Neural Networks (RNNs) in Deep Learning10 Minuten
Attention-Based LSTM (Long Short-Term Memory)10 Minuten
Capsule Networks in Deep Learning10 Minuten
Model Optimizers: Beyond ADAM10 Minuten

4 AufgabenInsgesamt 48 Minuten

Practice Quiz : Working of Recurrent Neural Networks (RNN)6 Minuten
Practice Quiz : LSTM Architecture and Working6 Minuten
Practice Quiz : Module Optimization and Compilation6 Minuten
Knowledge Check : Deep Learning with RNN, LSTM and Model Optimization30 Minuten

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on SLP, MLP, RNN, CNN, LSTM and many more complex deep learning concepts.

Das ist alles enthalten

1 Video1 Lektüre1 Aufgabe1 Diskussionsthema

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Dozent

Edureka

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Warum entscheiden sich Menschen für Coursera für ihre Karriere?

Felipe M.

Lernender seit 2018

„Es ist eine großartige Erfahrung, in meinem eigenen Tempo zu lernen. Ich kann lernen, wenn ich Zeit und Nerven dazu habe.“

Jennifer J.

Lernender seit 2020

„Bei einem spannenden neuen Projekt konnte ich die neuen Kenntnisse und Kompetenzen aus den Kursen direkt bei der Arbeit anwenden.“

Larry W.

Lernender seit 2021

„Wenn mir Kurse zu Themen fehlen, die meine Universität nicht anbietet, ist Coursera mit die beste Alternative.“

Chaitanya A.

„Man lernt nicht nur, um bei der Arbeit besser zu werden. Es geht noch um viel mehr. Bei Coursera kann ich ohne Grenzen lernen.“

Häufig gestellte Fragen

Deep learning is a subset of machine learning that emphasizes artificial neural network algorithms designed to mimic the structure and functions of the human brain. Multi-layered neural networks are developed to autonomously learn and identify features from vast datasets, enabling them to effectively perform tasks such as speech recognition, image recognition, and natural language processing. Deep learning plays a crucial role in AI advancements as it requires extensive amounts of data and computational strength.

The target audience for Practical Deep Learning with Python comprises beginners and intermediate learners eager to grasp and utilize deep learning methods with Python. This course is tailored for for data scientists, AI Research Analysts, and developers who possess fundamental programming skills and a basic grasp of machine learning principles.

To effectively follow the exercises and examples in Practical Deep Learning with Python, you will need a computer with the following minimum system requirements:

- Operating System: Windows, macOS, or Linux.

- Processor: A multi-core processor (preferably with support for AVX instructions).

- RAM: At least 8 GB of RAM, though 16 GB or more is recommended for larger datasets.

- Storage: At least 10 GB of free disk space to accommodate datasets, libraries, and project files.

- Python Environment: Python 3.6 or later installed with libraries such as TensorFlow or PyTorch, NumPy, Matplotlib, and Pandas.

Please note: All the practical are performed on Google Colab

To effectively learn deep learning, it is advisable to acquire the following essential knowledge and skills:

- Mathematics: A solid grasp of linear algebra (matrices, vectors), calculus (derivatives and gradients), probability, and fundamental statistics. These ideas are essential for grasping the workings of neural networks and the process of optimization.

- Programming Abilities: Mastery of Python is crucial, since the majority of deep learning frameworks, such as TensorFlow and PyTorch, are built on Python. Having knowledge of libraries like NumPy, Pandas, and Matplotlib is also advantageous.

- Machine Learning Essentials: Grasping the core principles of machine learning, including supervised and unsupervised learning, overfitting, underfitting, and evaluation metrics for models, will establish a solid groundwork.

Data Management: Familiarity with data preprocessing methods, such as addressing missing data, normalization, and data augmentation, is beneficial.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.