Robert K.
After 365:
Data Analysis Specialist | Nagel-Group
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Online Course
free
Convolutional Neural Networks with TensorFlow in Python
with
Nikola Pulev
and
Iskren Vankov
Master Convolutional Neural Networks: Building advanced neural network models with TensorFlow
8151 students already have enrolled
Skill level:
Advanced
Duration:
4 hours
-
Lessons (5 hours)
-
Practice exams (16 minutes)
- Lessons (5 hours)
- Practice exams (16 minutes)
CPE credits:
6
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master convolutional neural networks for visual recognition.
- Acquire advanced deep learning skills with TensorFlow.
- Use proven techniques to optimize neural network performance.
- Master TensorBoard for deep learning visualization.
- Apply TensorFlow to solve real-world computer vision problems.
Topics & tools
theorypythonprogrammingmachine learningdeep learningneural networkscomputer visiontensorflowtensorboardconvolutional neural networksimage processingmachine and deep learning
Your instructor
Data Scientist | Course Creator at 365 Data Science | University of Cambridge Physics graduate
Track record
Bringing real-world expertise from leading global companies
Academic background
Bachelor's degree, Natural Sciences, Physics
Media and recognition
- Silver Medal, International Physics Olympiad
- National and international awards in mathematics and physics competitions
Oxford Graduate | Quantum Computing Researcher | AI & Machine Learning Expert
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Computer Science
Media and recognition
- Published in Quantum Science and Technology
- Microsoft Research Award: Best Informatics Dissertation
- BSc CompSci & Physics Prize---top mark in program
- Graduate of Oxford, Caltech & Edinburgh, First Class Honors
Course OVERVIEW
Description
CPE Credits: 6
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
This course offers a deep dive into an advanced neural network construction – convolutional neural networks. First, we explain the concept of image kernels, and how it relates to CNNs. Then, you will get familiar with the CNN itself, its building blocks, and what makes this kind of network necessary for computer vision. You’ll apply the theoretical bit to the MNIST example using TensorFlow, and understand how to track and visualize useful metrics using TensorBoard in a dedicated practical section. Later in the course, you’ll be introduced to a handful of techniques to improve the performance of neural networks, and a huge real-world practical project for classifying fashion item pictures. Finally, we will cap it all off with an intriguing look through the history of the most influential CNN architectures.
Prerequisites
- Python (version 3.8 or later), TensorFlow 2 library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab)
- Intermediate Python and machine learning knowledge is required.
- Familiarity with NumPy and neural network fundamentals is recommended.
Advanced preparation
Curriculum
54 lessons
3 exams
- 2. Kernels17 MinHere, we introduce the concept of image kernels and transformations. We will explain how they work through convolution, which forms the basis for CNNs.
- 3. CNN Introduction23 MinThis is where we have an in-depth discussion of the Convolutional Neural Networks: you will understand the motivation and fundamental strength of this type of network and learn more about the concepts and layers that make it work – feature maps and pooling layers. Finally, you will discover how the dimensions change in such a network.
- 4. Neural network techniques (revision)9 MinIn this section, we quickly revise the main concepts of neural networks in general. These include activation functions, overfitting and early stopping, and optimizers. This part is intended only as a reference and is not a substitute for a full course on the basics of Machine Learning.
- 5. Setting up the environment3 MinIn this part of the course, you will learn how to set up Python 3 and load up Jupyter. We’ll also show you what the Anaconda Prompt is and how you can use it to download and import new modules.
- 6. CNN assembling - MNIST46 MinIn this section, you will put theory into practice. You will develop a simple CNN architecture, implement it from scratch, and train the model on the MNIST example.
- 7. Tensorboard: Visualization tool for TensorFlow38 MinAfter training a model to classify digits, we turn our attention to tracking and visualizing different metrics. The ability to analyze your models is crucial in Machine Learning, and here, we will lay the groundwork which makes that possible through TensorBoard.
- 8. Common techniques for better performance of neural networks19 MinThis section introduces 3 crucial concepts for improving the performance of neural networks in general. We will discuss theoretically L2 regularization and weight decay, Dropout, and Data Augmentation, which you will practice in the next section.
- 9. A practical project: Labelling fashion items82 MinSo far, you have already gained some practice with CNNs in a somewhat sterile environment. However, the real world is much messier than the MNIST example. That’s why, in this section, we have prepared a huge practical example inspired by tasks you are very likely to do in your job. You will practice classifying clothes and other fashion items with multiple labels. Here, you will see why the neat results from the MNIST example are almost unattainable and gain valuable insight into the procedure of undertaking such a project.Introduction to the problem FreeThe objective and the images FreeConverting images to arrays FreeGetting started with the code concepts FreePrimary classification task - Part 1 FreePrimary classification task - Part 2 FreePrimary classification task - Part 3 FreeGlasses and Sunglasses Homework FreeTrousers and Jeans - discussion of approaches FreeTrousers and Jeans - All FreeTrousers and Jeans - Gender + Type FreeTrousers and Jeans - Type - Homework FreeTrousers and Jeans - comparing the methods FreeShoes Homework FreeL2 regularization and Dropout FreeData augmentation - Shoes All FreePractice exam Free
- 10. Understanding CNNs7 MinIn this section, we briefly discuss what aspects of neural networks you need to be careful with, illustrating them with real examples of models that exhibit unexpected behavior.
- 11. Popular CNN architectures19 MinThis section is dedicated to exploring the most influential and breakthrough Convolutional Neural Networks of the past - AlexNet, VGG, GoogLeNet, and ResNet - and explaining the concepts behind their architectures. We will also look at the Computer Vision competition ILSVRC and its development over the years.
- 12. Course exam30 Min
Free preview
1.1 What does the course cover?
6 min
1.2 Why CNNs?
4 min
2.1 Introduction to image kernels
3 min
2.2 How do image transformations work?
7 min
2.3 Kernels as matrices
2 min
2.4 Convolution - applying kernels
2 min
94%
of AI and data science graduates
successfully change
or advance their careers.
9 in 10
people walk away career-ready
with practical data and AI skills.
$29,000
average salary increase
after moving to an AI and data science career
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