Deep Learning: Convolutional Neural Networks in Python

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

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

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 CNNusing 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 inNumpy,Matplotlib,andTensorflow. 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 wantmorethan 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

WHATORDERSHOULDITAKEYOURCOURSESIN?:

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

UNIQUEFEATURES

• 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