Mastering Recurrent Neural Networks, Theory and Practice in Python
Recurrent Neural Network
Python,IT & Software,Neural Networks
Lectures -96
Duration -13.5 hours
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Course Description
Recurrent Neural Networks (RNNs), a class of neural networks, are essential in processing sequences such as sensor measurements, daily stock prices, etc. In fact, most of the sequence modeling problems on images and videos are still hard to solve without Recurrent Neural Networks. Further, RNNs are also considered to be the general form of the deep learning architecture. Hence, the understanding of RNNs is crucial in all the fields of Data Science. This course addresses all these concerns and empowers you to take your career to the next level with a masterful grip on the theoretical concepts and practical implementations of RNNs in Data Science.
Why Should You Enroll in This Course?
The course ‘Mastering Recurrent Neural Networks, Theory and Practice in Python’ is crafted to help you understand not only how to build RNNs but also how to train them. This straightforward learning by doing course will help you in mastering the concepts and methodology with regards to Python.
The two mini-projects Automatic Book Writer and Stock Price Prediction, are designed to improve your understanding of RNNs and add more skills to your data science toolbox. Also, this course will enable you to immediately apply the skills you acquire to your own projects. This course is:
- Easy to understand.
- Expressive and self-explanatory.
- To the point.
- Practical with live coding.
- Thorough, covering the most advanced and recently discovered RNN models by renowned data scientists.
How Is This Course Different?
This is a practical course that encourages you to explore and experience the real-world applications of RNNs. The course starts with the basics of how RNNs work and then goes far deep gradually. So, if your ambition is to become a Python developer, this course is indispensable.
You are assigned Home Work/ tasks/ activities at the end of the subtopics in each module. The reason for this is to make your learning easier and also to assess and further build your learning based on the concepts and methods you have learned previously. Most of these activities are coding based, preparing you for implementing the concepts you learn at your workplace.
With a core understanding of RNNs, you can sharpen your deep learning skills and ensure emerging career growth. Data Science, as a career path, is certainly rewarding. You not only get the opportunity to solve some of the most interesting problems, but you are also assured of a handsome salary package.
This course presents you with a cost-effective option to learn the concepts and methodologies of RNNs with Data Science. Our tutorials are subdivided into a series of short, in-depth HD videos along with detailed code notebooks.
So, without further delay, get started with the course that simplifies complex concepts for you.
Teaching Is Our Passion:
We focus on creating online tutorials that encourage learning by doing. We aim to provide you with more than a superficial look at RNNs. For instance, the two mini-projects in the final module will help you to see for yourself via experimentation the practical implementation of RNNs in the real world. We have worked extra hard to ensure you understand the concepts clearly. We want you to have a sound understanding of the basics before you move onward to the more complex concepts. The course materials that make certain you accomplish all this include high-quality video content, course notes, meaningful course materials, handouts, and evaluation exercises. You can also get in touch with our friendly team in case of any queries.
After completing this course successfully, you will be able to:
- Relate the concepts and theories sequence modeling with RNNs.
- Understand the methodology of RNNs with Data Science using real datasets.
Who this course is for:
- People who want to take their data speak to the next level.
- People who want to master RNNs with real datasets in Data Science.
- People who want to implement RNNs in realistic projects.
- Individuals who are passionate about numbers and programming.
- Business Analysts.
- Data Scientists.
Goals
What will you learn in this course:
- The importance of Recurrent Neural Networks (RNNs) in Data Science.
- The important concepts from the absolute beginning with a comprehensive unfolding with examples in Python.
- The reasons to shift from classical sequence models to RNNs.
- Practical explanation and live coding with Python.
- An overview of concepts of Deep Learning Theory.
- Deep details of RNNs with examples and derivations.
- TensorFlow (Deep learning framework by Google).
- The use and applications of state-of-the-art RNNs (with implementations in state-of-the-art framework TensorFlow) that are much more recent and advanced in terms of accuracy and efficiency.
- Building your own applications for automatic text generation as well as for stock price prediction.
- And much more…
Prerequisites
What are the prerequisites for this course?
- No prior knowledge is needed. We will start from the basics and gradually build your knowledge in the subject.
- A willingness to learn and practice.
- Knowledge of Python will be a plus.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Course
3 Lectures
- Introduction to Instructor and Aisciences 12:18 12:18
- Introduction To Instructor 02:19 02:19
- Focus of the Course 08:55 08:55
Applications of RNN (Motivation)
7 Lectures
DNN Overview
22 Lectures
RNN Architecture
13 Lectures
RNN implementation
11 Lectures
Sentiment Classification using RNN
7 Lectures
Gradient Descent in RNN
9 Lectures
Vanishing Gradients
9 Lectures
TensorFlow
2 Lectures
Book Writer
7 Lectures
Stock Price Prediction
5 Lectures
Further Readings and Recourses
1 Lectures
Instructor Details
AISciences
We are a group of experts, PhDs, and Practitioners of Artificial Intelligence, Computer Science, Machine Learning, and Statistics. Some of us work in big companies like Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.
We decided to produce a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of Machine Learning, Statistics, Artificial Intelligence, and Data Science.
Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory and without a long reading. Today we also publish a more complete course on some topics for a wider audience.
Our courses have had phenomenal success. Our Courses have helped more than 100,000 students to master AI and Data Science.
✅ Stay Connected to Us.
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✅ For Business Inquires: contact@aisciences.io
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