Neural Networks with Tensorflow
Delve into the world of Neural Networks with TensorFlow! Learn how to create, train, and optimize neural networks for various AI applications. #NeuralNetworks #TensorFlow #AIEducation #Tech ▼
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Course Feature
Cost:
Paid
Provider:
Udemy
Certificate:
Paid Certification
Language:
English
Start Date:
2020-12-28
Course Overview
❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [August 31st, 2023]
What does this course tell?
(Please note that the following overview content is from Alison)
Youre going to learn the most popular library to build networks and machine learning algorithms In this hands-on practical course you will be working your way through with Python Tensorflow and Jupyter notebooksWhat you will learn:Basics of TensorflowArtificial NeuronsFeed Forward Neural NetworksActivations and Softmax OutputGradient DescentBackpropagationLoss FunctionMSEModel OptimizationCross-EntropyLinear RegressionLogistic RegressionConvolutional Neural Networks (with examples)Text and Sequence DataRecurrent Neural Networks (with examples)Neural Style Transfer (in progress)
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
In this course, participants will acquire skills and knowledge related to neural networks and machine learning algorithms using Python Tensorflow and Jupyter notebooks. They will learn the basics of Tensorflow, artificial neurons, feed forward neural networks, activations and softmax output, gradient descent, backpropagation, loss function, MSE, model optimization, cross-entropy, linear regression, logistic regression, convolutional neural networks (with examples), text and sequence data, recurrent neural networks (with examples), and neural style transfer (in progress).
lHow does this course contribute to professional growth?
This course provides a comprehensive introduction to Neural Networks with Tensorflow, allowing professionals to gain a better understanding of the fundamentals of machine learning algorithms and how to apply them in real-world scenarios. Through hands-on practical exercises, participants will learn the basics of Tensorflow, Artificial Neurons, Feed Forward Neural Networks, Activations and Softmax Output, Gradient Descent, Backpropagation, Loss Function, MSE, Model Optimization, Cross-Entropy, Linear Regression, Logistic Regression, Convolutional Neural Networks, Text and Sequence Data, Recurrent Neural Networks, and Neural Style Transfer. This course will help professionals to develop their skills in the field of machine learning and neural networks, allowing them to stay up-to-date with the latest advancements in the field.
Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education as it covers a wide range of topics related to neural networks and machine learning algorithms, such as artificial neurons, feed forward neural networks, activations and softmax output, gradient descent, backpropagation, loss function, model optimization, linear regression, logistic regression, convolutional neural networks, text and sequence data, recurrent neural networks, and neural style transfer.
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