Deep Learning - Artificial Neural Networks with TensorFlow

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In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

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39 on-demand videos & exercises
Level: Beginner
English
4hrs 47mins
Access on mobile, web and TV

What to know about this course

TensorFlow is the world’s most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI and machine learning. So, if you want to do deep learning, you got to know TensorFlow.

In this course, you will learn how to use TensorFlow 2 to build deep neural networks. We will first start by learning the basics of machine learning, classification, and regression. Then in the next section, we will understand the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning. In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization.

By the end of this course, we will have understood how to use TensorFlow for artificial neural networks in deep learning.


Who's this course for?

This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers.

One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib.


What you'll learn

  • Understand what machine learning is
  • Build linear models with TensorFlow 2
  • Learn how to build deep neural networks with TensorFlow 2
  • Learn how to perform image classification and regression with ANN
  • Learn loss functions such as mean-squared error and cross-entropy loss
  • Learn about stochastic gradient descent, momentum, and Adam optimization

Key Features

  • Understand the utilization of TensorFlow 2 to construct artificial neural networks
  • The course covers the basics of machine learning, classification, and regression
  • Explore the connection between artificial neural networks and biological neural networks

Course Curriculum

About the Author

Lazy Programmer

The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses.
His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue.
As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.