Deep Learning Basics with TensorFlow 2.0

What you'll learn in this course 📖

This course covers the basic syntax of TensorFlow 2.0 and the fundamentals of deep learning. TensorFlow 2.0 has a significantly different interface than version 1.0. Therefore, understanding the differences and leveraging their strengths will enable you to implement cleaner and more beautiful machine learning code.

If you want to build your own deep learning model, pay attention! ☺️

Are you having these concerns while learning deep learning?

• "I'm confused about the basic concepts of deep learning."
• "Instead of training deep learning models with copy and paste, I want to create my own."
• "I don't know the difference between TensorFlow 1.0 and 2.0."

Don't worry. This course will cover the fundamentals of deep learning with TensorFlow! I hope you'll gain a deeper, more intuitive understanding of deep learning concepts through hands-on practice, not just theory! 😝

How you will look after taking this course 📜

• You will be able to understand the basic concepts of deep learning.
• You can understand the features of TensorFlow 2.0.
• You will learn how to create your own code from scratch, rather than relying on someone else's code.

What's special about my lecture ✨

• A lecture that even beginners can easily follow and understand!
  Even if you don't have basic knowledge of TensorFlow, we'll teach you step-by-step so you can easily approach it.
• A journey of a thousand miles begins with a single step! Lectures focusing on basic grammar and philosophy.
  We've carefully selected and will share with you only the essential basics for deep learning.
• You can learn both simple and advanced implementations of TensorFlow models.
  You can apply the theory you learned about how deep learning models work through practical training.

What you'll learn in this course 📚

TensorFlow Basics

Learn about the concepts of tensors and variables, which are the foundation of TensorFlow, and explore the eager execution feature added in TensorFlow 2.0 and sequential models, which are a core fundamental feature of TensorFlow.

Deep Learning Basics

Learn about the fundamentals of deep learning: loss functions, regularization, optimization, automatic differentiation, and more.

TensorFlow 2.0 Advanced Techniques

Learn how to enhance deep learning capabilities with tf.function, word embeddings that represent text strings as numbers, and the Functional API, which utilizes functional APIs.

Deep learning advanced techniques

We'll learn about recurrent neural networks and convolutional neural networks, the core neural networks of deep learning, and how to save and restore model files.

I am this kind of person 😝

My career is as follows: