Deep Learning: Convolutional Neural Networks in Python
Deep Learning: Convolutional Neural Networks in Python Udemy Coupon Code by Lazy Programmer Inc. with 14 hours on-demand video course and free download resource. Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning
Convolutional Neural Networks (CNN) Course Overview
Deep Learning: Convolutional Neural Networks in Python
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
Learn about one of the most powerful Deep Learning architectures yet!
The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don’t exist in the real world!
This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
This course will teach you:
- The basics of machine learning and neurons (just a review to get you warmed up!)
- Neural networks for classification and regression (just a review to get you warmed up!)
- How to model image data in code
- How to model text data for NLP (including preprocessing steps for text)
- How to build an CNN using Tensorflow 2
- How to use batch normalization and dropout regularization in Tensorflow 2
- How to do image classification in Tensorflow 2
- How to do data preprocessing for your own custom image dataset
- How to use Embeddings in Tensorflow 2 for NLP
- How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.
This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
What you’ll learn
- Understand convolution and why it’s useful for Deep Learning
- Understand and explain the architecture of a convolutional neural network (CNN)
- Implement a CNN in TensorFlow 2
- Apply CNNs to challenging Image Recognition tasks
- Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis)
- Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
Recommended Deep Learning Courses
Deep Learning A-Z 2025: Neural Networks, AI & ChatGPT Prize Best seller
TensorFlow for Deep Learning Bootcamp Best seller
Deep Learning: Convolutional Neural Networks in Python Course Reviews
Everything You Need to Know About Deep Learning: Convolutional Neural Networks in Python
This course offers a comprehensive and well-structured introduction to Deep Learning. Lazy Programmers the instructor, brings a wealth of expertise in Development, making this course both informative and engaging.
The course structure is easy to follow. Each section, for example, covers a different aspect of Deep Learning Course, ensuring a logical progression through the material. It includes video lectures, readings, and hands-on exercises, which make complex concepts accessible and practical.
Moreover, the instructor explains each topic clearly and concisely. He supports the lessons with plenty of examples and exercises, which help students grasp the material effectively.
What I appreciated most about this course is its practical focus. For instance, the instructor emphasizes teaching skills and knowledge that are directly applicable to real-world scenarios. Additionally, students gain access to helpful resources such as templates, checklists, and cheat sheets.
Another standout feature is the platform itself. Udemy Coupons offers flexibility, allowing students to learn at their own pace and access course materials from anywhere with an internet connection. Furthermore, the multiple payment options make it easy for students to choose a plan that suits their budget.
In addition, the course community is highly active, with forums where students can ask questions and engage with peers. The instructor, consequently, is very responsive and addresses student inquiries and feedback promptly.
Overall, I highly recommend the Deep Learning: Convolutional Neural Networks in Python to anyone looking to learn Convolutional Neural Networks (CNN). This well-organized and practical course equips you with the skills and knowledge you need to succeed in this field.
