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?

1. Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.

2. Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.

3. Higher Developer Productivity: PyTorch’s design prioritizes developer productivity, promoting efficiency in building and experimenting with models.

4. Dynamic Approach for Graph Computation – AutoGrad: PyTorch’s dynamic computational graph through AutoGrad enables flexible and efficient model development.

5. GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.

Course Highlights:

Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.

Learn GPU programming and explore how to access free GPU resources for efficient learning.

Master the AutoGrad feature of PyTorch, a key aspect for dynamic graph computation.

Implement Deep Learning models using PyTorch, transitioning from theory to practical application.

Explore the basics of Convolutional Neural Networks (CNNs) in PyTorch, a fundamental architecture for computer vision tasks.

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!

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

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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