Overview What You Will Learn Curriculum Instructor

Course Overview

The goal of computer vision is to derive descriptive information about a scene by computer analysis of images of the scene. Vision algorithms can often serve as computational models for biological visual processes, and they also have many practical uses; this course treats computer vision as a subject in its own right. Vision problems are often ill-defined, ill-posed, or computationally intractable; nevertheless, successes have been achieved in many specific areas — document processing and industrial inspection.

In this course, you will understand images and how to prepare them. You will learn about the role of programs and data in the field of Artificial Intelligence. You will get a detailed knowledge of the two main technologies: Deep Learning and Convolutive Neural networks. You will come to know the advantages and disadvantages of deep learning. You will have a good basic level understanding of AI as well as ML.

With this course, you will begin to learn how to deal with images and what you can do about it, avoid data mistakes in a machine learning setting, know what Deep Learning is and finally build some cool TensorFlow applications like objects detection or face recognition.

What You Will Learn

  • Understand images and prepare them for your machine
  • Understand the role of data in any programs and his roles in A.I.
  • Tame the Deep Learning technology so that you can build your own A.I
  • Understand Pro and Cons in different Deep Learning architectures
  • Learn the state-of-the-art paper and be ready to implement them!

Program Curriculum

  • Deep Learning Frameworks
  • Installation
  • Chapter 1 Quiz
  • $7 Million Cybersecurity Scholarship by EC-Council

  • Image Acquisition
  • Image Basics
  • Image Enhancement
  • Color
  • Chapter 2 Quiz

  • Problem Setting
  • Training as a Solution
  • Validation
  • Data Quality
  • Splits
  • Types
  • Training Strategies
  • Chapter 3 Quiz

  • Feed Forward Neural Networks
  • Training a Neural Network
  • Regularization – I
  • Regularization – II
  • Regularization – III
  • Convolutive Neural Network
  • Optimization
  • Chapter 4 Quiz

  • Resnet
  • Resnet Paper
  • Resnet Training
  • U-Net
  • U-Net Paper
  • U-Net Network
  • Resnext
  • Resnext Paper
  • Resnext Implementation
  • Chapter 5 Quiz
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Instructor

Andrea Marisio

From his first Degree in Computer Science, Andrea has worked on Deep Learning technologies, from Recommender Systems to NLP and CV. He has read a lot of research papers, did courses, and delivered speeches in this field of expertise. Andrea has completed his bachelor’s as well as master’s degree in Computer Science from the University of Insubria. He is passionate about Deep Learning. Andrea possess excellent knowledge of Computer Vision and the technologies involved in it like PyTorch, Tensorflow, Keras. He is also an expert in the field of Machine Learning.

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