If you're looking to improve your skills in deep learning, specifically in recurrent neural networks, look no further than the course "Deep Learning: Recurrent Neural Networks in Python." With a high aggregate rating of 4.63124 and over 4500 reviews, this course is highly regarded by previous students. This robust course offers comprehensive coverage of various techniques using RNNs, such as time series forecasting, stock predictions, and natural language processing through AI.
What Will You Learn?
Before diving into the RNN and deep learning material, students receive a review of basic neurons, machine learning, and neural networks for classification and regression. From there, the course covers sequence data modeling, time series data modeling, text data modeling (including preprocessing steps for text), and how to build and use an RNN with TensorFlow 2. The course includes using a GRU and LSTM model with TensorFlow 2, time series forecasting, and even predicting stock prices and returns with LSTMs in TensorFlow 2---not your typical technique. The course's coverage of text includes embedding in TensorFlow 2 for natural language processing. Moreover, the course teaches how to build a text classification RNN, detailing examples such as spam detection, sentiment analysis, parts-of-speech tagging, and named entity recognition.
With this course's practical coverage of time series forecasting, students will gain a solid foundation in the basics of time series analysis and will also be familiar with natural language processing. Notable too is the in-depth pre-processing coverage which sets the stage for building RNN models. It is important to note that the course's approach to teaching RNNs and deep learning models places an emphasis on understanding the models' fundamentals.
Why Take This Course
It's challenging to find quality courses that offer a comprehensive foundation in RNNs and deep learning. While many programs offer instruction on writing codes with specific deep learning APIs, very few programs offer a detailed guide to the underlying models' mechanisms. This course is an exception and teaches its students how to build and understand RNNs using TensorFlow 2 and Python 3. By sufficing the students' expectations of what makes a good deep learning course through the explanation of every line of code in detail, this course prioritizes the ability of students to write working codes on their own without copying and pasting. This course's unique approach allows students to break down what they've learned, visualize the model internally, and create their applications without relying on the library.
In a crowded field of deep learning courses, this course stands out for its refreshing focus on core concepts over simple usage of APIs. The course's instructor emphasizes "seeing for yourself" through experimentation, allowing you to get beneath the surface of machine learning algorithms and understand every part of the process truly.
Suggested Prerequisites
Some things that students should have an understanding of before taking this course should include:
- Matrix addition and multiplication
- Basic probability (conditional and joint distributions)
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
These pre-requisites assume a higher level of math and coding ability, which gives students with previous coding experience a head start in this course.
Course Instructor
The instructor for this course has over 20 years of experience as a course review content writer for top-ranking course review sites. He has unique features that set his courses apart from others, like his emphasis on detailing every line of code and incrementally building on previous concepts rather than simply copying other courses' code. Students can easily reach out to the instructor for help and support. The instructor does not shy away from university-level mathematics and implements the motto: "If you can't implement it, you don't understand it."
Course Structure
This course is held entirely online. For students that enroll in the course, an initial packet of materials initially requires downloading and installing Tensorflow, Numpy, and Matplotlib. Once these materials are in place, the course is entirely self-paced, so students can take as little or as much time as needed to absorb the material. This package includes video lectures to break down concepts and visual aids and practice exercises that allow the students to get comfortable with coding.
Pros and Cons from Verified Students
Feedback from students who completed the course includes an appreciation of the instructor's detail-oriented approach and emphasis on understanding the models' mechanics. The pre-processing section has also received positive reviews, with many students commenting that they came away with a better understanding of pre-processing steps. The only drawback that students have noted is sometimes the course's concepts can be challenging to follow due to their depth but the positive feedback far outweighs this issue.
Is This Course Right For You?
If you're looking for a comprehensive guide to building and understanding RNNs and deep learning models, "Deep Learning: Recurrent Neural Networks in Python" is a great choice. This course is perfect for those who have previous experience in Python and are comfortable with mathematical concepts. This course's teaching technique allows you to advance your deep learning skills at your own tempo, without relying on the preset libraries and plugins of various deep learning APIs - you truly learn how to understand and build these models from scratch while still having the support of the online instructor. The course emphasizes a "learning-by-doing" approach that includes practice exercises and aids to help reinforce concepts. By following the suggested prerequisites, a motivated student can feel confident in their ability to master this course and become a talented deep learning specialist.
