PyTorch for Deep Learning Computer Vision Bootcamp 2024
PyTorch for Deep Learning Computer Vision Bootcamp 2024, available at $54.99, has an average rating of 4.25, with 85 lectures, based on 116 reviews, and has 7886 subscribers.
You will learn about Master how to Perform Computer Vision Task with Deep Learning Learn to Work with PyTorch Convolutional Neural Networks with Torch Library Build Intuition on Convolution Operation on Images Learn to Implement LeNet Architecture on CIFAR10 dataset which has 60000 images This course is ideal for individuals who are Software Developer or Machine Learning Practitioner or Data Scientist or Anyone interested to learn PyTorch or Anyone interested in Deep learning It is particularly useful for Software Developer or Machine Learning Practitioner or Data Scientist or Anyone interested to learn PyTorch or Anyone interested in Deep learning.
Enroll now: PyTorch for Deep Learning Computer Vision Bootcamp 2024
Summary
Title: PyTorch for Deep Learning Computer Vision Bootcamp 2024
Price: $54.99
Average Rating: 4.25
Number of Lectures: 85
Number of Published Lectures: 85
Number of Curriculum Items: 88
Number of Published Curriculum Objects: 88
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Master how to Perform Computer Vision Task with Deep Learning
- Learn to Work with PyTorch
- Convolutional Neural Networks with Torch Library
- Build Intuition on Convolution Operation on Images
- Learn to Implement LeNet Architecture on CIFAR10 dataset which has 60000 images
Who Should Attend
- Software Developer
- Machine Learning Practitioner
- Data Scientist
- Anyone interested to learn PyTorch
- Anyone interested in Deep learning
Target Audiences
- Software Developer
- Machine Learning Practitioner
- Data Scientist
- Anyone interested to learn PyTorch
- Anyone interested in Deep learning
Dive into Computer Vision with PyTorch: Master Deep Learning, CNNs, and GPU Computing for Real-World Applications – 2024 Edition”
Unlock the potential of Deep Learning in Computer Vision, where groundbreaking advancements shape the future of technology. Explore applications ranging from Facebook’s image tagging and Google Photo’s People Recognition to fraud detection and facial recognition. Delve into the core operations of Deep Learning Computer Vision, including convolution operations on images, as you master the art of extracting valuable information from digital images.
In this comprehensive course, we focus on one of the most widely used Deep Learning frameworks – PyTorch. Recognized as the go-to tool for Deep Learning in both product prototypes and academia, PyTorch stands out for its Pythonic nature, ease of learning, higher developer productivity, dynamic approach for graph computation through AutoGrad, and GPU support for efficient computation.
Why PyTorch?
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Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.
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Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.
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Higher Developer Productivity: PyTorch’s design prioritizes developer productivity, promoting efficiency in building and experimenting with models.
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Dynamic Approach for Graph Computation – AutoGrad: PyTorch’s dynamic computational graph through AutoGrad enables flexible and efficient model development.
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GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.
Course Highlights:
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Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.
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Learn GPU programming and explore how to access free GPU resources for efficient learning.
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Master the AutoGrad feature of PyTorch, a key aspect for dynamic graph computation.
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Implement Deep Learning models using PyTorch, transitioning from theory to practical application.
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Explore the basics of Convolutional Neural Networks (CNNs) in PyTorch, a fundamental architecture for computer vision tasks.
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Apply CNNs to real-world datasets, developing hands-on experience with practical applications.
Our Approach:
We believe that true learning extends beyond theoretical understanding; it involves building confidence through practical application. Throughout the course, we’ve incorporated assignments at the end of each section, enabling you to measure your progress and reinforce your learning. We aspire to empower you with the skills and confidence needed to navigate the dynamic field of Deep Learning in Computer Vision.
Embark on this journey with Manifold AI Learning, where innovation meets education. We look forward to welcoming you inside the course and witnessing your success. Best of luck!
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Manifold AI Learning
Course Curriculum
Chapter 1: Welcome Aboard
Lecture 1: Why PyTorch is Powerful
Chapter 2: Introduction
Lecture 1: Introduction to Pytorch
Lecture 2: Getting System Ready
Lecture 3: Create Tensors in Pytorch
Lecture 4: Tensor Slicing and Reshape
Lecture 5: Mathematical Operations on Tensors
Lecture 6: Numpy in Pytorch
Lecture 7: What is CUDA
Lecture 8: Pytorch on GPU
Lecture 9: Download Materials
Chapter 3: AutoGrad in Pytorch
Lecture 1: Autograd in Pytorch
Lecture 2: Implementing Gradient Descent using Autograd
Lecture 3: Download Materials
Chapter 4: Creating Deep Neural Networks in Pytorch
Lecture 1: Building first neural network
Lecture 2: Writing Deep neural network
Lecture 3: Writing Custom NN module
Lecture 4: Download Materials
Chapter 5: CNN on Pytorch
Lecture 1: Data Loading – CIFAR10
Lecture 2: Data Visualization
Lecture 3: CNN Recap
Lecture 4: First CNN
Lecture 5: CNN Deep layers
Lecture 6: Download Materials
Chapter 6: LeNet Architecture in Pytorch
Lecture 1: LeNet Overview
Lecture 2: LeNet Model in Pytorch
Lecture 3: Preparation & Evaluation
Lecture 4: Download Materials
Chapter 7: Optional Learning- Python Basics
Lecture 1: Why Computer Programming Language
Lecture 2: Why Python?
Lecture 3: Getting System Ready – Installing Jup[yter Notebook
Lecture 4: Jupyter Notebook – Tips & Tricks
Lecture 5: What is Covered in this section
Lecture 6: Variables in Python
Lecture 7: Print Function
Lecture 8: Numeric Data Type
Lecture 9: String Data Type
Lecture 10: Boolean Data Type
Lecture 11: Type Conversion & Type Casting
Lecture 12: Adding Comments in Python Programming Language
Lecture 13: Data Structures in Python
Lecture 14: Tuples & Sets in Python
Lecture 15: Python Dictionaries
Lecture 16: Conditional Statements in Python – if
Lecture 17: Conditional Statements in Python – While
Lecture 18: Inbuilt Functions in Python – range & input
Lecture 19: For Loops
Lecture 20: Functions in Python
Lecture 21: Classes in Python
Chapter 8: Mini Project with Python Basics
Lecture 1: Section Attachment
Lecture 2: Mini Project – Hangman
Lecture 3: Writing a class
Lecture 4: Mini Project – Continued
Lecture 5: Logic Building
Lecture 6: Logic for Single Letter input
Lecture 7: Final Testing
Chapter 9: Python for Data Science – Numpy
Lecture 1: Numpy Library Code
Lecture 2: Why Numpy?
Lecture 3: Numpy
Lecture 4: Resize & Reshape of Arrays
Lecture 5: Slicing
Lecture 6: Broadcasting
Lecture 7: Mathematical Operations & Functions in Numpy
Chapter 10: Python for Data Science – Pandas
Lecture 1: Section Attachments
Lecture 2: Pandas Library
Lecture 3: Pandas Dataframe
Lecture 4: Pandas Dataframe – Load from External file
Lecture 5: Working with null values
Lecture 6: Slicing Pandas Dataframe
Lecture 7: Imputation
Chapter 11: Python for DataScience – Matplotlib
Lecture 1: Section Attachments
Lecture 2: Matplotlib Introduction
Lecture 3: Format the plot
Lecture 4: Plot Formatting & Scatter Plot
Lecture 5: Histplot
Chapter 12: Additional Concepts from Machine Learning
Lecture 1: Bonus : How do you select a Model in ML
Lecture 2: Bonus – Get More from Learning Journey
Chapter 13: Machine Learning for Projects
Lecture 1: Understanding Generative AI
Lecture 2: Machine learning Deployment Part 1 – Model Prep – End to End
Lecture 3: Machine learning Deployment Part 2 – Deploy Flask App – End to End
Lecture 4: Streamlit Tutorial
Lecture 5: Bonus Content : References
Lecture 6: Packaging the ML Models
Lecture 7: Docker Containers for Data Science and ML Projects
Lecture 8: Course Trailer on MLOps
Instructors
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Manifold AI Learning ®
Learn the Future – Data Science, Machine Learning & AI
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 2 votes
- 3 stars: 19 votes
- 4 stars: 45 votes
- 5 stars: 49 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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