Build Neural Networks In Seconds Using Deep Learning Studio
- 4.4
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Brief Introduction
Develop Keras / TensorFlow Deep Learning Models Using A GUI And Without Knowing Python Or Machine LearningDescription
In this course you will Machine Learning And Neural Networks easily. We will develop Keras / TensorFlow Deep Learning Models using GUI and without knowing Python or programming.
If you are a python programmer, in this course you will learn a much easier and faster way to develop and deploy Keras / TensorFlow machine learning models.
You will learn about important machine learning concepts such as datasets, test set splitting, deep neural networks, normailzation, dropout, artificial networks, neural network models, hyperparameters, WITHOUT hard and boring technical explanations or math formulas, or follow along code. Instead, you will learn these concepts from practical and easy to follow along teaching methods.
In this course, Deep Learning Studio will produce all the python code for you in the backend, and you never even have to even look at it (unless of course you want to). By the end of this course you will be able to build, train and deploy deep learning AI models without having to do any coding.
After taking this course you will be able to produce well written professional python code without even knowing what python is or how to program, Deep Learning Studio will do all this work for you. Instead you can easily stay focused on building amazing artificial intelligence machine learning solutions without programming.
Also, if you just want to learn more about Deep Learning Studio and get a jump start on this revolutionary ststem, this is the course for you! Deep Learning Studio is just beginning to shake up the data science world and how artificial intelligence solutions are developed!
Get ahead of the curve by taking this exciting and easy to follow along course!
Requirements
- Requirements
- Interest In Machine Learning And Neural Networks.
- Interest Or Curiosity About Data Science.
Michael Kroeker
4.4 Raiting
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- 0.0
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