Neural Networks with TensorFlow - A Complete Guide!: 3-in-1
Learn the fundamentals of Neural Networks and TensorFlow with this comprehensive 3-in-1 course, and gain the skills to build powerful machine learning models! ▼
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Course Feature
Cost:
Paid
Provider:
Udemy
Certificate:
Paid Certification
Language:
English
Start Date:
2018-09-25
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)
This training program includes 3 courses to provide comprehensive training. The first course covers Neural Networks and Tensorflow, teaching how to build a flower recognition program and predict atomization energy of an atom. The second course covers Advanced Neural Networks with Tensorflow, exploring Deep Reinforcement Learning algorithms, Autoencoders, and Siamese neural networks. The third course covers high-level concepts such as CNN and RNN, and how to take them to production. By the end of the course, you'll be able to build powerful Deep Learning models and scale them as required.
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?
By taking this course, learners will acquire the skills and knowledge to build powerful Deep Learning models, accelerate the training of their models, and scale them as required. They will also learn to implement different kinds of Neural Networks, such as simple feedforward Neural Networks, multi-layered perceptrons, CNNs, RNNs, and more. Learners will also gain an understanding of advanced Neural Networks with TensorFlow, such as Deep Reinforcement Learning algorithms, Generative Networks, and Deep Q Learning. Additionally, they will learn to implement Autoencoder applications and train generative models. Finally, learners will gain an understanding of high-level concepts such as neural networks, CNN, RNN, and NLP, and learn how to take their models to production.
lHow does this course contribute to professional growth?
This course contributes to professional growth by providing a comprehensive 3-in-1 training program that covers the fundamentals of Neural Networks with TensorFlow. It provides a solution-based approach to learning, with each topic being explained with the help of a real-world example. The course covers topics such as building a simple flower recognition program, predicting atomization energy, handwritten number recognition, and estimating celebrity looks. It also covers advanced topics such as Deep Reinforcement Learning algorithms, Generative Networks, Deep Q Learning, Autoencoders, and Siamese neural networks. By the end of the course, participants will have a better understanding of how to leverage the power of TensorFlow to train neural networks of varying complexities.
Is this course suitable for preparing further education?
This course is suitable for preparing further education in Neural Networks with TensorFlow. It covers important high-level concepts such as neural networks, CNN, RNN, and NLP. It also provides hands-on experience with real-world datasets to get a better understanding of neural network programming. By the end of the course, students will be able to build powerful Deep Learning models, accelerate the training of their models, and scale them as required.
Course Syllabus
Learning Neural Networks with Tensorflow
Advanced Neural Networks with Tensorflow
TensorFlow for Neural Network Solution
Course Provider
Provider Udemy's Stats at AZClass
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