Deep Learning: Convolutional Neural Networks in Python

Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning

4.63 (5789 reviews)

Udemy

platform

English

language

Data Science

What you will 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

Why take this course?

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.

Suggested Prerequisites:

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

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

Screenshots

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Our review

📚 **Course Review: Understanding Convolutional Neural Networks (CNNs) and Deep Learning with TensorFlow** **Overview:** The course has received an average global rating of 4.61, with all recent reviews being positive. It is designed to teach learners about the fundamentals and advanced concepts of CNNs and deep learning using TensorFlow. The course is praised for its clear explanations, practical examples, and the instructor's teaching style. **Pros:** - **Explanatory Clarity:** The explanations provided in the course are clear, straightforward, and have been highly appreciated by learners. The instructor's energy and approach to lessons make them enjoyable and easy to follow. - **Effective Q&A:** The Q&A section is responsive and useful, with questions being answered promptly, contributing positively to the learning experience. - **Practical Application:** The additional practices offered in the course are beneficial for learners to apply what they've learned and enhance their understanding of the concepts. - **Comprehensive Content:** The course covers a wide range of topics within deep learning, including CNNs, providing a thorough understanding of the subject matter. - **Math Explanation:** The math behind the algorithms and code is explained well, allowing learners to implement concepts on their own and diagnose issues effectively. - **Real-world Focus:** The course connects theoretical knowledge with practical implementation, which has been a standout feature for many learners. - **Detailed Examples:** Hands-on examples are provided that can be worked on independently, offering a robust learning experience. - **Educative Approach:** The course focuses on educating learners about data handling and real-world challenges in machine learning, going beyond theoretical explanations. **Cons:** - **Code Availability:** Some learners felt that the code used in lessons was not immediately available, which they believe could streamline the learning process by fostering immediate practical application. - **Bureaucratic Pacing:** A few reviews suggested that certain material placed at the beginning could have been introduced earlier to avoid any confusion or unnecessary repetition. - **Lecture Length:** A learner using Windows noted that shorter lectures might be more beneficial due to differences in function usage. - **Q&A Feedback:** One review mentioned an instance where the instructor's response to a student's question seemed to linger on a personal anecdote, which some learners feel could be condensed for more educational content. - **Visual Aids:** Some visual learners suggested that connecting explanations to code more visually could enhance comprehension and implementation of the concepts. - **Course Structure:** A few reviews indicated that the course could benefit from some trimming in the latter sections, which appear to repeat information already covered extensively in the introduction. **Additional Feedback:** - The course is considered a great starting point for individuals new to programming in Python and delving into deep learning concepts. - Learners who have taken other courses on classification/regression before this one found it beneficial to take this course as a follow-up to deepen their understanding of CNNs and image processing within the context of computer vision. **Conclusion:** This course is highly recommended for learners interested in mastering the principles of CNNs and deep learning with TensorFlow. Its strengths lie in its clarity, practical examples, and comprehensive coverage of the subject. While there are a few areas that could be improved, such as immediate code availability and potential streamlining of content, the overall feedback points to a course that is both valuable and effective for learners at various levels of their deep learning journey. 🌟

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