Deep Learning: Recurrent Neural Networks with Python

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 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 ‘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 a course will help you in mastering the concepts and methodology with regard 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.

Course Content:

The comprehensive course consists of the following topics:

1. Motivations

a. What can a Recurrent Neural Network (RNN) Do?

i. Real-World Applications

b. When to model RNN?

i. Images

ii. Videos

iii. Speech

2. Deep Neural Networks: An Overview

a. Perceptron

i. Convolution

ii. Bias

iii. Activation

iv. Loss

v. Back Propagation

vi. Exercises

b. Multilayered Perceptron

i. Why Multilayered Architecture?

ii. Universal Approximation Theorem

iii. Overfitting in DNNs

iv. Early stopping

v. Dropout

vi. Stochastic Gradient Descent

vii. Mini Batch Gradient Descent

viii. Batch Normalization

ix. Optimization Algorithms

x. Exercises

3. Recurrent Neural Networks (RNNs)

a. Architecture of an RNN

i. Recurrent Connections

ii. Weight Sharing

iii. Many to One

iv. One to Many

v. Many to Many

vi. Exercises

b. Gradient Descent in RNNs

i. Derivatives

ii. Back Propagation

iii. Worked Example

iv. Exercises

c. Vanishing Gradients in RNN

i. Why Vanishing Gradients in RNN is more common?

ii. Why tanh activations for hidden layers

iii. Gated Recurrent Unit (GRU)

iv. Exercises

d. Modern RNNs

i. Long Short Term Memory (LSTM)

ii. Bi-Directional RNNs

iii. Attention Based Models

iv. Exercises

e. Introduction to TensorFlow

i. Implementing RNNs

ii. Exercises

4. Projects:

a. Automatic Book Writer

b. Stock Price Prediction

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.