Computer Vision with PyTorch Practice Exam

Computer Vision with PyTorch Practice Exam

About the Computer Vision with PyTorch Exam

Computer Vision with PyTorch teaches how to build and train deep learning models for image processing and analysis. Using PyTorch’s powerful framework, learners will explore convolutional neural networks (CNNs), transfer learning, and object detection techniques. This course provides hands-on experience in developing computer vision applications such as image classification, facial recognition, and real-time image processing, equipping learners with the skills to create AI-driven vision solutions.

Skills Required to learn 

• Basic understanding of Python programming.
• Familiarity with machine learning concepts and deep learning fundamentals.
• Knowledge of linear algebra, probability, and statistics.
• Experience with data manipulation using libraries like NumPy and Pandas.
• Basic understanding of neural networks and how they work.
• Familiarity with PyTorch or any deep learning framework (optional but helpful).

Knowledge Gained

• Understanding of computer vision fundamentals and image processing techniques.
• Proficiency in using PyTorch to build and train deep learning models for vision tasks.
• Hands-on experience with convolutional neural networks (CNNs) for image classification and object detection.
• Knowledge of transfer learning and fine-tuning pre-trained models for improved performance.
• Skills in implementing real-time image recognition and facial detection systems.
• Experience in handling and preprocessing image datasets for deep learning applications.

Who should take the Exam?

• Aspiring AI and machine learning engineers looking to specialize in computer vision.
• Data scientists and analysts interested in deep learning for image processing.
• Software developers aiming to build AI-powered vision applications.
• Researchers and academicians working on computer vision projects.
• IT professionals transitioning into AI and deep learning roles.
• Students and professionals preparing for careers in artificial intelligence and computer vision.
• Anyone seeking certification to validate their expertise in PyTorch and computer vision.

Course Outline

Welcome Aboard

• Course Introduction
• Why Is PyTorch Powerful?

Introduction to PyTorch and Tensors

• What Is PyTorch
• Diving into PyTorch
• Installing PyTorch
• Create Tensors in PyTorch
• Tensor Slicing and Reshape
• Mathematical Operations on Tensors
• NumPy in PyTorch
• What Is CUDA
• PyTorch on GPU

AutoGrad in PyTorch

• AutoGrad in PyTorch
• AutoGrad in a Loop

Creating Deep Neural Networks in PyTorch

• Building the First Neural Network
• Writing a Deep Neural Network
• Writing a Custom NN Module

CNN in PyTorch

• Data Loading - CIFAR10
• Data Visualization
• CNN Recap
• First CNN
• CNN Deep Layers

LeNet Architecture in PyTorch

• LeNet Overview
• LeNet Model in PyTorch
• Preparation and Evaluation
• Python Basics

Why Learn Any Programming Language

• Why Choose Python
• Installing Jupyter Notebook
• Jupyter Notebook - Tips and Tricks
• What We Will Cover in This Section
• Variables in Python
• Print Function
• Numerical Data Types and Arithmetic Operations in Python
• String Data Type
• Boolean Data Type
• Type Conversion and Type Casting
• Adding Comments in Python Programming Language
• Data Structures in Python
• Tuples and Sets in Python
• Python Dictionaries
• Conditional Statements in Python - if
• Conditional Statements in Python - While
• Inbuilt Functions in Python - range and input
• For Loops
• Functions in Python
• Classes in Python

Mini Project with Python Basics

• Mini Project - Hangman
• Writing a Class
• Mini Project - Continued
• Logic Building
• Logic for Single-Letter input
• Final Testing

Python for Data Science – with NumPy

• NumPy
• Resize and Reshape Arrays
• Slicing
• Broadcasting
• Mathematical Operations and Functions in NumPy

Python for Data Science – with Pandas

• Pandas Library
• Pandas Dataframe
• Pandas Dataframe - Load from External File
• Working with Null Values
• Slicing Pandas Dataframe
• Imputation

Python for Data Science – with Matplotlib

• Matplotlib Introduction
• Format the Plot
• Plot Formatting and Scatter Plot
• Histplot