Deep Learning: Convolutional Neural Networks in Python
Deep Learning: Convolutional Neural Networks in Python
Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning
Product Brand: Udemy
4.7
This Udemy course is a comprehensive and well-structured introduction to Deep Learning: Convolutional Neural Networks in Python. The instructor, Lazy Programmers Inc. is a leading expert in the field with a wealth of experience in Convolutional Neural Networks (CNN) to share. in this udemy free courses you will Learn Tensorflow 2 Convolutional Neural Networks (CNN) for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning.
Table of Contents
Deep Learning: Convolutional Neural Networks in Python Course
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
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Who this course is for:
- Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP
- Software Engineers and Data Scientists who want to level up their career
FAQ (Frequently Asked Questions) about this course
Are there any limitations to access Deep Learning: Convolutional Neural Networks in Python coupon Code?
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What types of Downloadable resources are included in the course?
Deep Learning: Convolutional Neural Networks in Python course includes a variety of resources, such as: Video lectures, Coding exercises and quizzes, Downloadable code files and project templates, PDF guides and notes
Can I get a refund for Deep Learning: Convolutional Neural Networks in Python, If I purchased with a coupon?
Yes, Udemy offers a 30-day money-back guarantee for all courses purchased on the website, even if you used a coupon to purchase the course.
Can I get a certificate after completing the course?
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Taught by Lazy Programmer Inc.
