The course, "PyTorch for Deep Learning Computer Vision Bootcamp 2024", is a PyTorch-enabled complete video course for the year 2024 that ushers in the new era of cognitive interaction with visual data. Learn all about the convolution operation as an important aspect of image processing and become conversant with PyTorch AutoGrad’s dynamic features that allow computation through graphs to run smoothly.
Learn why Deep Learning practitioners choose PyTorch over other tools. The system's Pythonic design and interface eliminate entry barriers experienced by beginners, making learning easier. Gain first-hand experience of how useful GPU acceleration is when handling large datasets and complex models. Also, learn free resources to maximize your learning of GPU programming.
Regarding practical implementation, our module, “PyTorch for Deep Learning Computer Vision Bootcamp 2024,” teaches how to use Pytorch to build cutting-edge deep learning models. Get into convolutional neural networks (CNNs), which are the building blocks of computer vision, and put your skills into practice using real-world datasets. By having practical assignments at the end of each section, we ensure you grasp the principles and traverse confidently across this ever-changing landscape called Computer Vision under the broader umbrella term Learning.
PyTorch for Deep Learning Computer Vision Bootcamp 2024 Table of Contents:
- Why PyTorch is powerful (03:51)
- Introduction to PyTorch (01:24)
- Getting the system ready (08:06)
- Creating Tensors in PyTorch (06:19)
- Tensor Slicing and Reshape (03:25)
- Mathematical Operations on Tensors (02:15)
- Numpy in PyTorch (04:35)
- What is CUDA (04:04)
- PyTorch on GPU (07:12)
- Download Materials (00:01)
- Assignment on PyTorch Basics (8 questions)
- Autograd in PyTorch (12:04)
- Implementing Gradient Descent using Autograd (04:51)
- Download Materials (00:01)
- Assignment on Autograd (1 question)
- Building the first neural network (08:11)
- Writing a Deep neural network (04:26)
- Writing Custom NN module (06:10)
- Download Materials (00:01)
- Assignment on Deep Neural Networks (2 questions)
- Data Loading - CIFAR10 (10:53)
- Data Visualization (04:35)
- CNN Recap (03:44)
- First CNN (07:45)
- CNN Deep layers (07:37)
- Download Materials (00:01)
- LeNet Overview (03:46)
- LeNet Model in PyTorch (11:25)
- Preparation & Evaluation (08:50)
- Download Materials (00:01)
- Why Computer Programming Language (05:45)
- Why Python? (02:38)
- Getting the System Ready - Installing Jupyter Notebook (07:19)
- Jupyter Notebook - Tips & Tricks (05:56)
- What is Covered in this section (01:52)
- Variables in Python (08:36)
- Print Function (03:27)
- Numeric Data Type (05:02)
- String Data Type (03:48)
- Boolean Data Type (01:55)
- Type Conversion & Type Casting (06:18)
- Adding Comments in Python Programming Language (02:05)
- Data Structures in Python (09:11)
- Tuples & Sets in Python (08:25)
- Python Dictionaries (05:22)
- Conditional Statements in Python - if (11:16)
- Conditional Statements in Python - While (06:08)
- Inbuilt Functions in Python - range & input (07:43)
- For Loops (04:36)
- Functions in Python (09:16)
- Classes in Python (12:17)
- Section Attachment (00:01)
- Mini Project - Hangman (06:18)
- Writing a class (07:54)
- Mini Project - Continued (05:22)
- Logic Building (06:18)
- Logic for Single Letter input (10:06)
- Final Testing (06:48)
- Numpy Library Code (00:01)
- Why Numpy? (00:25)
- Numpy (16:21)
- Resize & Reshape of Arrays (08:42)
- Slicing (06:04)
- Broadcasting (12:35)
- Mathematical Operations & Functions in Numpy (07:13)
- Section Attachments (00:02)
- Pandas Library (16:17)
- Pandas DataFrame (05:28)
- Pandas DataFrame - Load from External file (08:28)
- Working with null values (06:20)
- Slicing Pandas DataFrame (05:42)
- Imputation (03:44)
- Section Attachments (00:01)
- Matplotlib Introduction (09:54)
- Format the plot (08:12)
- Plot Formatting & Scatter Plot (03:27)
- Histplot (06:57)
- Bonus: How do you select a Model in ML (23:16)
- Bonus - Get More from Learning Journey (00:15)
- Understanding Generative AI (01:17:44)
- Machine Learning Deployment Part 1 - Model Prep - End to End (15:31)
- Machine Learning Deployment Part 2 - Deploy Flask App - End to End (11:48)
- Streamlit Tutorial (19:22)
- Bonus Content: References (01:03)
- Packaging the ML Models (56:26)
- Docker Containers for Data Science and ML Projects (59:15)
- Course Trailer on MLOps (04:22)
Who is this course for?
- Software Developer
- Machine Learning Practitioner
- Data Scientist
- Anyone interested to learn PyTorch
- Anyone interested in Deep learning
Click on the links below to Download PyTorch for Deep Learning Computer Vision Bootcamp 2024!
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