If you're interested in deep learning and computer vision, you need to dive deep into Convolutional Neural Networks (CNNs) architecture. It's one of the most powerful architectures that exist for image processing, object detection, and segmentation. Studying this architecture will give you a thorough understanding of how it works.
Course Overview
The Deep Learning: Convolutional Neural Networks in Python course is designed to help you hone your skills and give you a comprehensive understanding of the CNN system. It's a course for beginners and seasoned professionals alike who want to delve into the technology behind deep learning and its practical applications, like object detection and image segmentation. The course is enriched with several practical examples to ensure you have a clear understanding of how the CNN works.
The course introduces you to the basics of convolution and how it is used in deep learning. It also helps in understanding and mastering techniques such as data augmentation, batch normalization, and modern archetypes. The course teaches practical skills like image classification, data preprocessing, and text classification for NLP, including spam detection, named entity recognition, parts-of-speech tagging, and sentiment analysis.
The course material is available to download for free, and all the software and applications used in the course like Tensorflow 2, Numpy, and Matplotlib are free as well. This comprehensive course aims to develop your critical thinking and logical deduction skills, enabling you to visualize the CNN architecture internally. The course doesn't follow the "remembering facts" method; instead, it emphasizes "seeing through experimentation."
Course Content
The course is divided into several parts that help students start at the basics and gradually develop full-blown convoluted neural networks. The course content has been highly reviewed, and the lecture "Machine Learning and AI" Prerequisite Roadmap" is available in the FAQ of any of the courses.
The prerequisite courses required to take this CNN course include matrix addition and multiplication, basic probability (conditional and joint distributions), Python coding (if/else, loops, lists, dicts, sets), and Numpy coding (matrix and vector operations, loading a CSV file).
Here's a detailed look at each part of the course:
Part I: Basics of Machine Learning in CNNs
The first part of the course delves into the basics of machine learning and how CNNs are important in computer vision. The course instructor reviews neuron-related topics like artificial neural networks, feed-forward artificial neural networks, and backpropagation.
The course explains the importance of convolution and pooling, activation functions, and hyperparameters in depth. The course also covers a comprehensive mathematical foundation of what convolution means and how it helps in image processing and recognition.
Part II: Image Processing Techniques
Part two focuses on modern image processing techniques used in deep learning and goes into detail on how to model image data in code. The course instructor explains modern techniques such as data augmentation and how batch normalization is useful in Tensorflow 2. Finally, you will learn how to build archetypes such as VGG yourself.
Part III: Natural Language Processing (NLP) Techniques
Part three focuses on natural language processing (NLP) techniques and covers modeling text data for NLP, including preprocessing steps for text and how to use embeddings in Tensorflow 2. This section also elaborates the importance of batch normalization and dropout regularization in Tensorflow 2. It teaches you how to build a Text Classification CNN for NLP.
Part IV: Building CNNs for Computer Vision
The final part of the course covers building a CNN using Tensorflow 2 for Computer Vision. It teaches image classification in Tensorflow 2 and data preprocessing for your own custom Image Dataset. This section also covers including hands-on practical skills on how to use Embeddings in Tensorflow 2 for NLP.
Reviews and Ratings
The Deep Learning: Convolutional Neural Networks in Python course has received 5171 reviews from verified students with a rating of 4.57694. The high rating serves as evidence of how much the students have enjoyed and learned from the course content. Several students mention how the course helped them gain an in-depth understanding of how CNNs work and how to apply the skills in their professional roles.
The course has acquired a reputation for being taught with all of the materials explained in detail. It doesn't waste time typing on the keyboard. Instead, the instructor presents important details about algorithms. The course is university-level math-taught, so he doesn't miss out on crucial details.
Unique Features of the Course
The Deep Learning: Convolutional Neural Networks in Python course is designed to help students learn right from the basics. The course instructor aims to make the students' journey as satisfactory as possible and prepares them to participate independently in data science projects.
The course stands out from other courses, where lectures are mere recitations. Every line of code is explained in detail in this course, and the instructor is available to answer students' questions and help them along their data science journey.
The course shows how to build and understand models, not merely how to use them. It encourages students to see for themselves via experimentation and visualize what's happening internally in the model.
Wrapping Up
Convolutional Neural Networks are the most powerful deep learning architecture used to obtain state-of-the-art results in computer vision tasks. The Deep Learning: Convolutional Neural Networks in Python course is here to guide your journey in learning the essential aspects of the CNN architecture.
This comprehensive course helps in gaining theoretical knowledge, hands-on experience, and practical skills in using CNNs for Computer Vision and Natural Language Processing. The reviews and rating of the course show how useful the lectures and content are in developing a thorough understanding of CNNs. This course will unquestionably help you level up your skills and prepare to take on more advanced topics.
