Udemy - TensorFlow 2.0 Practical Advanced

What you'll learn
Build, train, test and deploy Advanced Artificial Neural Networks (ANNs) models using Google's newly released TensorFlow 2.0.
Understand the underlying theory and mathematics behind Generative Adversarial Neural Networks (GANs).
Apply revolutionary GANs to generate brand new images using Keras API in TF 2.0.
Understand the underlying theory and mathematics behind Auto encoders and Variational Auto Encoders (VAEs).
Train and test Auto-Encoders to perform image compression and de-noising using Keras API in TF 2.0.
Understand the underlying theory and mathematics behind DeepDream algorithm. Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces using Keras API in TF 2.0!
Understand the intuition behind Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs).
Train Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text using Keras API in TF 2.0!
Apply transfer learning to transfer knowledge from pre-trained MobileNet and ResNet networks to classify new images using TensorFlow 2.0 Hub.
Develop ANNs models and train them in Google's Colab while leveraging the power of GPUs and TPUs.
Deploy AI models in practice using TensorFlow 2.0 Serving.
PC with internet connection
Recommended - The Ultimate Tensorflow 2.0 Practical Course
Description
Google has recently released TensorFlow 2.0 which is Google's most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of super advanced AI techniques in a much easier and faster way.
The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Advanced Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. This course will cover advanced, state-of-the-art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text translation, image de-noising, image compression, text-to-image translation, image segmentation, and image captioning.
The global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020. The technology is progressing at a massive scale and being adopted in almost every sector. The course provides students with practical hands-on experience in training Advanced Artificial Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to
Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces!
Implement revolutionary Generative Adversarial Networks known as GANs to generate brand new images.
Develop Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text!
Deploy AI models in practice using TensorFlow 2.0 Serving.
Apply Auto-Encoders to perform image compression and de-noising.
Apply transfer learning to transfer knowledge from pre-trained networks to classify new images using TensorFlow 2.0 Hub.
The course is targeted towards students wanting to gain a fundamental understanding of how to build, train, test and deploy advanced models in Tensorflow 2.0. Basic knowledge of programming and Artificial Neural Networks is recommended. Students who enroll in this course will master Advanced AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems.
Who this course is for
Data Scientists who want to apply their knowledge on Real World Case Studies
AI Developers
AI Researchers

Homepage

https://www.udemy.com/course/tensorflow-2-practical-advanced/

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