Python Pytorch Programming With Coding Exercises
Published 11/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Master Deep Learning with PyTorch Through Hands-On Coding Challenges
What you’ll learn
How to build, train, and evaluate neural networks using PyTorch.
Techniques for optimizing deep learning models, including regularization and transfer learning.
Implementation of CNNs and RNNs for complex tasks in image and sequence data.
Practical skills in applying PyTorch to real-world deep learning projects.
Requirements
A basic understanding of Python programming.
Familiarity with fundamental machine learning concepts.
Description
Overview
Section 1: Introduction to PyTorch
Lecture 1 Introduction to PyTorch
Lecture 2 Lesson 01
Lecture 3 Coding Exercises
Section 2: PyTorch for Linear Algebra
Lecture 4 PyTorch for Linear Algebra
Lecture 5 Lesson 02
Lecture 6 Coding Exercises
Section 3: Building Neural Networks from Scratch
Lecture 7 Building Neural Networks from Scratch
Lecture 8 Lesson 03
Lecture 9 Coding Exercises
Section 4: Deep Learning with PyTorch
Lecture 10 Deep Learning with PyTorch
Lecture 11 Lesson 04
Lecture 12 Coding Exercises
Section 5: Working with Data in PyTorch
Lecture 13 Working with Data in PyTorch
Lecture 14 Lesson 05
Lecture 15 Coding Exercises
Section 6: Optimization Techniques
Lecture 16 Optimization Techniques
Lecture 17 Lesson 06
Lecture 18 Coding Exercises
Section 7: Advanced Neural Network Architectures
Lecture 19 Advanced Neural Network Architectures
Lecture 20 Lesson 07
Lecture 21 Coding Exercises
Section 8: Customizing Models with PyTorch
Lecture 22 Customizing Models with PyTorch
Lecture 23 Lesson 08
Lecture 24 Coding Exercises
Section 9: Deploying PyTorch Models
Lecture 25 Deploying PyTorch Models
Lecture 26 Lesson 09
Lecture 27 Coding Exercises
Section 10: Project – End-to-End Deep Learning Pipeline
Lecture 28 Project – End-to-End Deep Learning Pipeline
Lecture 29 Lesson 10
Lecture 30 Coding Exercises
Aspiring data scientists and AI enthusiasts looking to learn deep learning.,Machine learning engineers who want to expand their skills with PyTorch.,Python developers interested in applying their programming skills to the field of AI and deep learning.
