Deep Learning for Computer Vision

Explore the field of computer vision using deep learning in this comprehensive course. We cover key areas including image classification, object detection, segmentation, image synthesis, and video analysis. We start with the basics and work our way up to advanced topics such as popular neural network architectures and how to develop your own.

Available formats: live-online
Deep Learning for Computer Vision

What will I learn?

  • Understand what computer vision is and what are the basic concepts of computer vision.
  • Refresh your knowledge of deep learning in preparation of learning how to use it for computer vision.
  • Learn how to use already finished popular models and how to create new custom ones.
  • Complete at least one full capstone project, combining the knowledge gained in this course to solve a complex computer vision problem.


Review of Deep Learning Concepts

Revisit basic concepts in Deep Learning and how they are implemented in Python. Learn the advantages and disadvantages of transfer learning.

Introduction to Computer Vision

Learn about the typical tasks covered under the umbrella of computer vision.

Convolutional Neural Networks

Learn the ins and outs of CNN architectures. Study the most popular CNN architectures for computer vision.

Autoencoders, Generative Adversarial Networks (GANs), Transformers and Diffusion Models

Learn about autoencoders, GANs, transformers and diffusion models: their applications, popular architectures and how to use them in the field of computer vision.

Object Recognition and Image Synthesis

Study various computer vision tasks in detail, from image classification techniques, to object detection. Learn how to create new images using various techniques.

Deep Learning and Video Analysis

Learn how to perform video analysis. Learn how to detect objects, classify actions and more.

Capstone Projects

Apply what you learned in the course in two projects covering image segmentation and video classification.

Why Edlitera?

Build the coding, data and AI skills you need, online, on your own schedule. From learning to code as a beginner to mastering cutting-edge data science, machine learning and AI techniques.

Learning for the real world

Our courses are made with the input and feedback of top teams at Fortune 500 companies in Silicon Valley and on Wall Street.

No-fluff learning

Each minute of each course is packed full of insight, best practices and real-world experience from our expert instructors.

Learn by doing

Start writing code on your computer from Day One. Practice on hundreds of exercises. Apply your skills in mini-projects. Get instant feedback from video solutions.

Complete learning tracks

With over 150 hours of video lectures and hundreds of practice exercises and projects, our learning tracks will help you level up your skills whether you are a novice or an advanced learner.

Course Syllabus

Review of Deep Learning Concepts

  • Basics of artificial neural networks
  • Basics of Deep Learning in Python
  • Stages of a typical Deep Learning project
  • Introduction to Transfer Learning
  • Advantages, disadvantages and applications of Transfer Learning

Introduction to Computer Vision

  • What is Computer Vision?
  • Analyzing and processing images for deep learning
  • Analyzing and processing videos for deep learning
  • Image classification
  • Object detection in images
  • Object segmentation in images
  • Image synthesis
  • Video analysis

Convolutional Neural Networks

  • Introduction to convolutional neural networks
  • Parts of a convolutional neural network
  • Convolution and convolutional layers
  • Pooling and pooling layers
  • Activation functions
  • Loss functions
  • Standard networks with dense layers
  • Fully convolutional Networks
  • Applications of convolutional neural networks
  • Common applications of CNNs
  • Applications of CNNs in the field of computer vision
  • Popular CNN architectures
  • Using finished architectures over custom architectures
  • Basics of the top 10 most popular CNN architectures
  • Leveraging the power of transfer learning

Autoencoders, Generative Adversarial Networks (GANs), Transformers and Diffusion Models

  • Introduction to Autoencoders
  • Parts of an Autoencoder
  • Autoencoder input layer, bottleneck, and output layer
  • Different types of Autoencoders
  • Denoising Autoencoders
  • Sparse Autoencoders
  • Deep Autoencoders
  • Undercomplete Autoencoders
  • Convolutional Autoencoders
  • Variational Autoencoders
  • Applications in the field of computer vision
  • Popular Autoencoder architectures
  • Introduction to GANs
  • Parts of a GAN
  • Structure of a typical GAN
  • Generator
  • Discriminator
  • Applications of GANs
  • Common Applications of GANs
  • Applications in the field of computer vision
  • Popular Gan Architectures
  • Intro to Transformers
  • General architecture of a vanilla Transformers network
  • Vision Transformer network (ViT)
  • Image classification using Vision Transformers
  • Transfer learning with Vision Transformers
  • Diffusion models

Object Recognition and Image Synthesis

  • Introduction to image classification
  • Binary image classification
  • Multiclass image classification
  • Multilabel image classification
  • Introduction to object detection
  • Object localization
  • How does object detection work?
  • Applications of object detection
  • Popular architectures of object detection
  • Introduction to object segmentation
  • How does image segmentation work?
  • Semantic segmentation
  • Instance segmentation
  • Applications of object segmentation
  • Popular architectures of object segmentation
  • Introduction to image synthesis
  • How does image synthesis work?
  • Applications of image synthesis
  • Using GANs for image synthesis
  • Using diffusion models for image synthesis

Deep Learning and Video Analysis

  • Introduction to working with videos
  • Videos as a type of data
  • What is a video?
  • Basics of video analysis
  • Videos as a data type in deep learning
  • Video analysis tasks
  • Object tracking
  • Action classification
  • Neural networks used for analyzing videos
  • CNNs and video analysis
  • Recurrent networks and video analysis
  • Popular architectures
  • Specialized architectures

Capstone Projects

  • Project 1: Image Segmentation
  • Project 2: Video Classification

What people are saying

"I walked into the bootcamp with some basic Python syntax and walked out with a much stronger, contextualized grasp of Python, an understanding of common mistakes, the ability to solve basic coding problems, and confidence in my ability to learn more."

Randi S., Edlitera Student
Randi S., a graduate of Edlitera's Python training bootcamp

"I wanted to learn Python and be able to process data without being tied and limited by Excel and macros. These classes gave me all the tools to do so and beyond. The materials provided, the engagement of the class by the tutors and their availability to help us were excellent."

Gaston G., Edlitera Student
Gaston G., a graduate of Edlitera's Python training bootcamp

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