This course offers a comprehensive introduction to PyTorch and deep learning for computer vision, with sections on Python fundamentals for those new to the language or needing a refresher. The curriculum begins with PyTorch basics, followed by instructions on accessing free GPU resources and coding on GPU.
Recommended experience
What you'll learn
Apply gradient descent using AutoGrad.
Analyze the LeNet architecture.
Develop a mini-Python project game.
Utilize NumPy, Pandas, and Matplotlib libraries.
Skills you'll gain
Details to know
Add to your LinkedIn profile
September 2024
5 assignments
See how employees at top companies are mastering in-demand skills
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 12 modules in this course
In this module, we will introduce you to the course, outlining what you can expect and why learning PyTorch is beneficial for diving into deep learning and computer vision. We’ll provide a brief overview of the course structure and demonstrate the power of PyTorch through a quick demo.
What's included
2 videos1 reading
In this module, we will explore PyTorch, starting with a brief introduction to its core features and functionality. We will delve into the concept of tensors, explaining their importance in deep learning, and demonstrate practical applications of tensors within the PyTorch framework.
What's included
1 video
In this module, we will dive deep into practical aspects of using PyTorch. Starting with installation on Google Colab, we will cover creating and manipulating tensors, performing mathematical operations, and integrating NumPy arrays. We will also explore CUDA, understanding its role and leveraging GPU acceleration to enhance computational efficiency.
What's included
7 videos1 assignment
In this module, we will delve into the AutoGrad functionality in PyTorch, understanding its role in automatic differentiation and gradient computation. We will demonstrate how to implement AutoGrad within loops, optimizing neural network training processes. Additionally, we will explore the computational graphs generated by AutoGrad, providing deeper insights into its operation and efficiency in deep learning tasks.
What's included
2 videos
In this module, we will guide you through the process of creating deep neural networks using PyTorch. Starting with building your first neural network, we will then move on to writing more complex deep neural networks. Finally, we will teach you how to design and implement custom neural network modules, providing you with the skills to tailor networks to your specific requirements.
What's included
3 videos
In this module, we will focus on Convolutional Neural Networks (CNNs) in PyTorch. You will learn how to load and preprocess the CIFAR10 dataset, visualize data for better insights, and review the fundamentals of convolution operations. We will guide you through building your first CNN and then advance to developing deeper CNN architectures, performing a series of convolution operations to achieve the desired output.
What's included
5 videos1 assignment
In this module, we will explore the LeNet architecture, starting with an overview of its structure and historical importance. You will learn how to implement the LeNet model in PyTorch and then proceed to train and evaluate it for practical applications. Additionally, we will discuss how LeNet compares with other CNN architectures and how to optimize its performance through effective preparation and evaluation methods.
What's included
3 videos
In this module, we will cover the foundational aspects of Python programming, starting with why learning a programming language is essential and the specific advantages of using Python. You will learn to install and navigate Jupyter Notebook, enhancing your coding experience. This module will also delve into Python basics, including variables, data types, arithmetic operations, strings, Booleans, type conversion, and comments. Further, we will explore Python’s data structures like tuples, sets, and dictionaries, and control flow statements such as "if," "while," and "for" loops. Finally, we will cover functions and classes in Python, providing a comprehensive introduction to Python programming.
What's included
21 videos
In this module, we will apply the Python basics learned so far by creating a mini project: the Hangman game. Starting with an introduction to the project, we will develop the necessary classes and objects. We will then proceed to implement the game's logic incrementally, focusing on handling single-letter inputs and other functionalities. Finally, we will conduct thorough testing and debugging to ensure the project runs as expected, consolidating your understanding of Python programming through this hands-on exercise.
What's included
6 videos1 assignment
In this module, we will delve into using NumPy for data science applications. You will learn how to create and manipulate arrays, resize and reshape them as needed, and perform slicing operations to select specific data subsets. Additionally, we will cover the concept of broadcasting, enabling you to apply operations across arrays of different shapes. Finally, we will explore various mathematical operations and functions that NumPy offers, enhancing your data manipulation and analysis capabilities.
What's included
5 videos
In this module, we will dive into the Pandas library, a powerful tool for data science in Python. You will learn about creating and managing Pandas DataFrames, essential for structured data analysis. We will cover how to load data from external files, manage null values, and use slicing operations to retrieve specific data elements. Additionally, we will discuss imputation techniques to address missing data, ensuring your datasets are clean and ready for analysis.
What's included
6 videos
In this module, we will explore Matplotlib, a fundamental library for data visualization in Python. You will learn how to create and format plots, enhancing their clarity and presentation. We will cover the creation and customization of scatter plots for in-depth data analysis, as well as generating histograms to visualize data distributions. By the end of this module, you will be equipped to utilize various plot types and formatting options to effectively present your data insights.
What's included
4 videos2 assignments
Instructor
Offered by
Recommended if you're interested in Machine Learning
Why people choose Coursera for their career
New to Machine Learning? Start here.
Open new doors with Coursera Plus
Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.