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January 28, 2025
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This course is part of Deep Learning: Recurrent Neural Networks with Python Specialization
Instructor: Packt - Course Instructors
Included with
Recommended experience
Beginner level
Ideal for data scientists, ML engineers, and AI enthusiasts with basic Python and statistics knowledge; ML experience helpful but not required.
Recommended experience
Beginner level
Ideal for data scientists, ML engineers, and AI enthusiasts with basic Python and statistics knowledge; ML experience helpful but not required.
Utilize PyTorch to build and optimize AI models.
Examine the effectiveness of gradient descent and hyperparameter tuning in model optimization.
Develop and apply RNN models for complex tasks such as speech recognition and machine translation.
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September 2024
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Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies.
You’ll start with the basics of RNNs and their applications, followed by an examination of DNNs, including their architecture and implementation using PyTorch. You will master building and deploying sophisticated AI models, develop RNN models for tasks like speech recognition and machine translation, understand and implement DNN architectures, and utilize PyTorch for model building and optimization. By the end, you'll have a robust knowledge of RNNs and DNNs and the confidence to apply these techniques in real-world scenarios. Designed for data scientists, machine learning engineers, and AI enthusiasts with basic programming (preferably Python) and statistics knowledge, this course combines theory with practical application through video lectures, hands-on exercises, and real-world examples.
In this module, we will introduce you to the course instructor, providing insights into their background and expertise. Additionally, we will outline the primary focus and objectives of the course, setting the stage for your learning journey in AI sciences.
2 videos2 readings
In this module, we will delve into the diverse applications of Recurrent Neural Networks (RNNs). You will learn to recognize human activities in videos, generate image captions, perform machine translation, and implement speech recognition. We will also explore using RNNs for stock price predictions and determine appropriate scenarios for modeling RNNs.
7 videos
In this module, we will explore the fundamentals of Deep Neural Networks (DNNs) and their implementation using PyTorch. You will learn about the architecture and representational power of DNNs, understand the importance of activation functions, and get hands-on experience with perceptrons. We will also cover gradient descent techniques, loss functions, and optimization strategies for building and refining DNN models.
45 videos1 reading1 assignment
Packt helps tech professionals put software to work by distilling and sharing the working knowledge of their peers. Packt is an established global technical learning content provider, founded in Birmingham, UK, with over twenty years of experience delivering premium, rich content from groundbreaking authors on a wide range of emerging and popular technologies.
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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.
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
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Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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