Online Course
Convolutional Neural Networks with TensorFlow in Python

Name: Convolutional Neural Networks in Python Course
Price: 36 USD

with Nikola Pulev and Iskren Vankov

Master Convolutional Neural Networks: Building advanced neural network models with TensorFlow

4.9

808 reviews on

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8376 students already have enrolled

Skill level:

Advanced

Duration:

4 hours

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

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

Nikola Pulev

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

Ask Nikola a question

Iskren Vankov

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

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Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 6 Field of Study: Information Technology
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

1. Introduction to the course

10 min

10 min

In this first section, we discuss the topics covered throughout this course along with the numerous benefits of learning about Convolutional Neural Networks.

What does the course cover? Free

Why CNNs? Free
2. Kernels

17 min

17 min

Here, we introduce the concept of image kernels and transformations. We will explain how they work through convolution, which forms the basis for CNNs.

Introduction to image kernels Free

How do image transformations work? Free

Kernels as matrices Free

Convolution - applying kernels Free

Edge handling Free
3. CNN Introduction

23 min

23 min

This 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.

CNNs motivation

Feature maps

Pooling and Stride

Dimensions
4. Neural network techniques (revision)

9 min

9 min

In 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.

Activation functions

Overfitting and early stopping

Optimizers

Practice exam
5. Setting up the environment

3 min

3 min

In 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.

Setting up the environment - Do not skip, please!

Installing the packages
6. CNN assembling - MNIST

46 min

46 min

In 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.

Road plan

A simple CNN architecture

Preprocessing the data

MNIST CNN Homework

Building and training the CNN

Testing the trained CNN
7. Tensorboard: Visualization tool for TensorFlow

38 min

38 min

After 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.

Tensorboard on the MNIST example

Confusion matrix and visualizing it with Tensorboard

Confusion Matrix Homework

Using Tensorboard to tune hyperparameters

Hyperparameter Tuning Homework
8. Common techniques for better performance of neural networks

19 min

19 min

This 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.

Introduction

Regularization

L2 Regularization and weight decay

Dropout

Data augmentation
9. A practical project: Labelling fashion items

82 min

82 min

So 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

The objective and the images

Converting images to arrays

Getting started with the code concepts

Primary classification task - Part 1

Primary classification task - Part 2

Primary classification task - Part 3

Glasses and Sunglasses Homework

Trousers and Jeans - discussion of approaches

Trousers and Jeans - All

Trousers and Jeans - Gender + Type

Trousers and Jeans - Type - Homework

Trousers and Jeans - comparing the methods

Shoes Homework

L2 regularization and Dropout

Data augmentation - Shoes All

Practice exam
10. Understanding CNNs

7 min

7 min

In 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.

Unexpected failures
11. Popular CNN architectures

19 min

19 min

This 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.

Introduction - the ILSVRC challenge

AlexNet - CNN success

VGG - more layers

GoogleNet - computational efficiency

ResNet - revolution of depth
12. Course exam

30 min

30 min

Course exam

Free lessons

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

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365 Data Science is far better than other platforms I’ve tried. The curriculum is professional, comprehensive, and highly practical—covering both theory and hands-on skills that are directly useful at work. It’s also much more cost-effective without sacrificing quality. Thanks to 365, I’ve been able to apply my learning to real-world projects with confidence. The platform makes advanced data education accessible while maintaining depth and clarity.

Robert K.

After 365: Data Analysis Specialist | Nagel-Group

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The explanation in this course is top-notch. Thank you, 365 Data Science!

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9 in 10

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Very well designed, well narrated course. Starts from scratch and ends up at the advanced levels. The icing on the cake is the integration of My SQL with Tableau at the end of the course which is a very important aspect of today's Data Analytics field. I have personally saved the last 10 practice exercises to dive in some days later, thus it will enable me to revise the course in the process.