Python Pytorch Programming With Coding Exercises

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

Welcome to Python PyTorch Programming with Coding Exercises, a dynamic course designed to equip you with the skills and knowledge required to excel in deep learning using the powerful PyTorch framework. PyTorch is one of the most popular and widely used deep learning libraries, trusted by researchers and developers worldwide for its flexibility and efficiency in building neural networks.In today’s rapidly evolving tech landscape, deep learning has become a critical skill, driving advancements in AI, machine learning, and data science. Understanding PyTorch is essential for anyone looking to delve into deep learning, as it offers a seamless way to design, implement, and optimize neural networks. This course is essential for those who aim to stay at the forefront of AI and machine learning.This course is meticulously structured to guide you through the fundamental and advanced concepts of PyTorch, with a focus on practical application through coding exercises. You’ll explore a wide range of topics, including:Introduction to PyTorch and its significance in the deep learning ecosystem.Building and training neural networks from scratch using PyTorch.Implementing various layers and activation functions for customized model architectures.Training, validation, and testing of deep learning models.Handling overfitting with regularization techniques and optimizing model performance.Understanding and implementing convolutional neural networks (CNNs) and recurrent neural networks (RNNs).Working with datasets and data loaders for efficient training.Transfer learning and fine-tuning pre-trained models for specific tasks.Each coding exercise is designed to reinforce your understanding of these concepts, ensuring that you not only learn but also apply PyTorch to solve real-world deep learning problems.Instructor Introduction: Your instructor, Faisal Zamir, brings over 7 years of experience in teaching Python and deep learning. As a Python developer and educator, Faisal has successfully guided countless students in mastering complex programming concepts, making this course both accessible and deeply informative.Certificate at the End of the Course: Upon successfully completing the course, you will receive a certificate of completion. This certificate will validate your expertise in using PyTorch for deep learning, enhancing your professional credibility and career prospects.

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.