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Updated in [February 21st, 2023]
Get up and running with deep learning with keras, a high level deep learning API
What you'll learn:
Simple implementation of convolutional neural networks, deep neural networks, recurrent neural networks, and linear regression
Understanding of keras syntax
Understanding of different deep learning algorithms
When I started learning deep learning, I had a hard time figuring out how everything worked. What library was the best for me? Which algorithms worked best for which data set? How could I know my model was accurate? I spent a lot of time on tutorials, courses and reading to try and answer these questions.In the end, I felt like the process I took to learn deep learning was too inefficient. That is why I created this course.
Learn Keras: Build 4 Deep Learning Applications
is a course that I designed to solve the problems my past self had. This course is designed to get you up and running with deep learning as quickly as possible. We use keras in this course because it is one of the easiest libraries to learn for deep learning. Each video, we go over a different machine learning algorithm and its use cases. The four algorithms we focus on the most are:
1. Linear Regression
2. Dense Neural Networks
3. Convolutional Neural Networks
4. Recurrent Neural Networks
In conclusion, if you are looking at a quick intro into deep learning, this course is for you.
So what are you waiting for? Let's get started!
(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
Summarizing the meaning of users learning this course, it is designed to get learners up and running with deep learning as quickly as possible. The course focuses on four algorithms: linear regression, dense neural networks, convolutional neural networks, and recurrent neural networks.
Possible Development Paths include pursuing a career in data science, machine learning, or artificial intelligence. Learners can also use the knowledge gained from this course to develop their own deep learning applications.
Learning Suggestions for learners include taking additional courses on deep learning, such as courses on natural language processing, computer vision, and reinforcement learning. Learners can also practice their skills by participating in online competitions or building their own projects. Additionally, learners should stay up to date with the latest advancements in deep learning by reading research papers and attending conferences.
