Applied Deep Learning with Python - Online Course
An 8-Hour Livestream Seminar Taught by
Ross JacobucciWednesday, May 8 –
Thursday, May 9, 2024
10:30am-12:30pm (convert to your local time)
1:00pm-3:00pm
This is one of two seminars offered by Professor Jacobucci on advanced machine learning methods. While this seminar focuses on applying deep learning algorithms to text and image data, along with insight into how deep learning methodologies can shape the design of studies, the other seminar, Advanced Machine Learning with R aims to provide you with the core knowledge needed to apply and evaluate advanced algorithms. Register for one or both.
Machine learning has revolutionized the collection and analysis of complex data types such as text, images, and videos. To extract meaningful insights from these data, it has become essential to move beyond conventional statistical methods and embrace the advanced capabilities of deep learning architectures, particularly neural networks. Python is currently the dominant programming language for research and applications using deep learning due to its rich ecosystem of algorithms and robust community support.
This workshop provides hands-on experience in applying deep learning algorithms to text and image data and insight into how deep learning methodologies, such as reinforcement learning, can shape the design of studies, providing guidance on their integration into research methodologies.
Starting May 8, we are offering this seminar as an 8-hour synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two 2-hour lecture sessions which include hands-on exercises, separated by a 30-minute break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
We will introduce several common deep learning frameworks, including recurrent and convolutional neural networks, pre-trained large language models for sentiment analysis and text classification (e.g., BERT), and models for image classification and object detection (e.g., CLIP). Emphasis will be placed on the theoretical background of these methods and their practical applications in real-world scenarios.
The scope of applications for deep learning will be demonstrated by applying deep learning algorithms to:
- Study the language used in Reddit posts from different communities (subreddits) and develop a model that can accurately identify which subreddit a post belongs to based on its content.
- Develop a system capable of recognizing and labeling objects and text present in smartphone screenshots.
- Constructing both reinforcement learning and active learning environments that enable dynamic adjustments in study designs based on real-time feedback and insights.
By the end of the workshop, participants will have the expertise to apply these cutting-edge deep-learning techniques to their data, driving forward the frontiers of research and development in their respective fields.
We will introduce several common deep learning frameworks, including recurrent and convolutional neural networks, pre-trained large language models for sentiment analysis and text classification (e.g., BERT), and models for image classification and object detection (e.g., CLIP). Emphasis will be placed on the theoretical background of these methods and their practical applications in real-world scenarios.
The scope of applications for deep learning will be demonstrated by applying deep learning algorithms to:
- Study the language used in Reddit posts from different communities (subreddits) and develop a model that can accurately identify which subreddit a post belongs to based on its content.
- Develop a system capable of recognizing and labeling objects and text present in smartphone screenshots.
- Constructing both reinforcement learning and active learning environments that enable dynamic adjustments in study designs based on real-time feedback and insights.
By the end of the workshop, participants will have the expertise to apply these cutting-edge deep-learning techniques to their data, driving forward the frontiers of research and development in their respective fields.
Computing
An introductory understanding of Python is presumed, but to ensure all participants are on equal footing, a video covering Python basics will be provided before the course starts, requiring 1-2 hours of study. This will lay the groundwork for understanding the more complex Python code used in the course.
We will focus on implementing neural network models within a Python environment, primarily using the PyTorch framework.
An introductory understanding of Python is presumed, but to ensure all participants are on equal footing, a video covering Python basics will be provided before the course starts, requiring 1-2 hours of study. This will lay the groundwork for understanding the more complex Python code used in the course.
We will focus on implementing neural network models within a Python environment, primarily using the PyTorch framework.
Who should register?
This course is designed for individuals who have previously worked with or are keen to explore image, text, or other innovative data types. We assume familiarity with introductory machine learning concepts, such as cross-validation.
This course is designed for individuals who have previously worked with or are keen to explore image, text, or other innovative data types. We assume familiarity with introductory machine learning concepts, such as cross-validation.
Seminar outline
Day 1:
-
- Concepts
- Introduction to neural network frameworks
- Transformers (BERT; GPT)
- Applications and Exercises
- Applying neural networks alongside ensemble methods
- Processing text
Day 2:
-
- Concepts
- Modeling images (convolutional neural networks; OCR; CLIP)
- Using deep learning in study design (active and reinforcement learning)
- Applications and Exercises
- Applying pre-trained models to text
- Analyzing screenshots from smartphones
- Deep learning and study design
Day 1:
-
- Concepts
- Introduction to neural network frameworks
- Transformers (BERT; GPT)
- Applications and Exercises
- Applying neural networks alongside ensemble methods
- Processing text
- Concepts
Day 2:
-
- Concepts
- Modeling images (convolutional neural networks; OCR; CLIP)
- Using deep learning in study design (active and reinforcement learning)
- Applications and Exercises
- Applying pre-trained models to text
- Analyzing screenshots from smartphones
- Deep learning and study design
- Concepts
Payment information
The fee of $695 includes all course materials.
**Email info@statisticalhorizons.com to receive a $200 total discount when you register for both of Professor Jacobucci’s advanced machine learning courses.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $695 includes all course materials.
**Email info@statisticalhorizons.com to receive a $200 total discount when you register for both of Professor Jacobucci’s advanced machine learning courses.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.