Deep Learning: Convolutional Neural Networks in Python
Udemy · Lazy Programmer Inc. · Updated
AI Tutor Rating
8.3/10
Duration
14 hours video
Classes
79
TensorFlow 2 CNNs for Computer Vision, Natural Language Processing and more. Deep Learning for Data Science and Machine Learning.
Deep Learning: Convolutional Neural Networks in Python is a Udemy course by Lazy Programmer Inc. that provides 14 hours of video instruction across 79 lectures. It focuses on building convolutional neural networks using TensorFlow 2 for applications in computer vision, natural language processing, and other domains. The course targets learners with Python and basic machine learning knowledge who want to master CNNs for tasks like image classification and object detection. It includes a certificate of completion and is priced at $13.99.
What you'll learn in Deep Learning: Convolutional Neural Networks in Python
Our Review of Deep Learning: Convolutional Neural Networks in Python
This course is structured as a comprehensive, project-focused journey through convolutional neural networks. The curriculum moves from an introduction to deep learning through mastering CNN architecture, applied TensorFlow, and building with computer vision, culminating in a 'Putting It All Together' section. This suggests a logical progression from foundational concepts to practical implementation, though the 79 lectures indicate a dense, lecture-heavy format typical of the instructor's style.
The teaching format relies exclusively on video content, which may suit visual learners but lacks interactive coding exercises or assessments. The depth is substantial, covering not just computer vision but also NLP applications, indicating a broader scope than many introductory CNN courses. However, the prerequisite of Python and basic ML means it is not for absolute beginners. For $13.99, the value proposition hinges on the volume of content and the included certificate, which may satisfy professional development requirements, though the learning experience is entirely self-directed through video.
The outcomes suggest a learner who completes Deep Learning: Convolutional Neural Networks in Python will be able to build and deploy CNN models for image classification and object detection using TensorFlow 2, and adapt these architectures to other domains like NLP. This positions the course as a practical toolkit for data scientists and ML engineers looking to add CNN implementation to their skillset, rather than a theoretical deep dive.
Pros and cons of Deep Learning: Convolutional Neural Networks in Python
Pros
- Comprehensive 14-hour curriculum covering CNN fundamentals to advanced applications
- Focus on practical TensorFlow 2 implementation for immediate project use
- Broad application scope including computer vision and natural language processing
- Includes a certificate of completion for professional development
- Affordable pricing at $13.99 for substantial video content volume
Things to consider
- Requires existing Python and basic machine learning knowledge, excluding beginners
- Teaching format is exclusively video lectures without interactive components
- Dense 79-lecture structure may feel overwhelming without guided practice
Who should take Deep Learning: Convolutional Neural Networks in Python?
This course best serves data scientists, machine learning engineers, or software developers with foundational Python and ML skills who need to quickly implement convolutional neural networks using TensorFlow 2. It fits professionals seeking to add practical CNN capabilities for computer vision or NLP projects to their toolkit through a structured, application-focused curriculum.
Course curriculum for Deep Learning: Convolutional Neural Networks in Python
Deep Learning: Convolutional Neural Networks in Python at a glance
| Provider | Udemy |
|---|---|
| Instructor | Lazy Programmer Inc. |
| Level | Intermediate |
| Time to complete | 14 hours video |
| Pricing | $13.99 |
| Certificate | Certificate |
| Prerequisites | Python, basic ML |
Fit
Best for
Not ideal for
The bottom line on Deep Learning: Convolutional Neural Networks in Python
Deep Learning: Convolutional Neural Networks in Python delivers substantial, practical CNN content at an accessible price point. It successfully equips learners with TensorFlow 2 implementation skills for real-world tasks, though its video-only format and prerequisite requirements limit its audience. For those with the foundational knowledge, it represents efficient skills acquisition.
Deep Learning: Convolutional Neural Networks in Python: frequently asked questions
What exactly does the Deep Learning: Convolutional Neural Networks in Python course teach you?
This Udemy course teaches you to build convolutional neural networks using TensorFlow 2. You will master CNNs for image classification and object detection, learn to apply them to natural language processing, and develop deep learning models for computer vision applications.
What background do I need before taking this CNN course on Udemy?
You need knowledge of Python programming and basic machine learning concepts before enrolling. The course prerequisites do not list advanced mathematics or deep learning theory, focusing instead on practical implementation with TensorFlow 2.
Does the Deep Learning: Convolutional Neural Networks in Python course offer a certificate and is it worth the price?
Yes, the course includes a certificate of completion. At $13.99 for 14 hours of video instruction across 79 lectures, it offers substantial content volume for the price, making it a cost-effective option for skill acquisition if the video format suits your learning style.
How does this Udemy CNN course compare to other online deep learning courses?
Compared to typical alternatives, this course emphasizes practical TensorFlow 2 implementation over theory and expands beyond computer vision to include NLP applications. Its 14-hour, project-structured curriculum is more focused on building complete models than exploring algorithmic variations.
How can I get the most value from the Deep Learning: Convolutional Neural Networks in Python course?
To maximize value, ensure you meet the Python and basic ML prerequisites first. Follow the curriculum sequentially from introduction to the final 'Putting It All Together' section, and apply the TensorFlow 2 code examples to your own computer vision or NLP projects as you progress.