🔅 Deep Learning Foundations: Natural Language Processing with TensorFlow

🌐 Author: Harshit Tyagi
🔰 Level: Intermediate

⏰ Duration: 1h 47m

🌀 Learn foundational deep learning techniques to classify, predict, and generate text using different neural networks.

📗 Topics: Natural Language Processing, TensorFlow, Deep Learning

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📱Machine Learning and Artificial intelligence
📱Deep Learning Foundations: Natural Language Processing with TensorFlow

🔅 Applied AI: Building NLP Apps with Hugging Face Transformers

🌐 Author: Kumaran Ponnambalam
🔰 Level: Intermediate

⏰ Duration: 57m

🌀 Learn how to build natural language processing (NLP) applications with pretrained transformers in Hugging Face, the popular machine learning platform.

📗 Topics: Natural Language Processing, Applied Machine Learning

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📱Machine Learning and Artificial intelligence
📱Applied AI: Building NLP Apps with Hugging Face Transformers

🔅 Advanced NLP with Python for Machine Learning

🌐 Author: Derek Jedamski
🔰 Level: Advanced

⏰ Duration: 2h 9m

🌀 Build upon your foundational knowledge of natural language processing (NLP) by exploring more complex topics such as word2vec, doc2vec, and recurrent neural networks.

📗 Topics: Natural Language Processing, Machine Learning, Python

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📱Advanced NLP with Python for Machine Learning

🔅 TensorFlow: Working with NLP

🌐 Author: Jonathan Fernandes
🔰 Level: Intermediate

⏰ Duration: 41m

🌀 Learn about using transformers in natural language processing with TensorFlow.

📗 Topics: Natural Language Processing, TensorFlow

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📱Machine Learning and Artificial intelligence
📱TensorFlow: Working with NLP

☄️ What is an Artificial Neural Networks?

Artificial neural networks (ANN) give machines the ability to process data similar to the human brain and make decisions or take actions based on the data. While there’s still more to develop before machines have similar imaginations and reasoning power as humans, ANNs help machines complete and learn from the tasks they perform.

▶️Content:

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001 What is Deep Learning
002 Plan of Attack
003 The Neuron
004 The Activation Function
005 How do Neural Networks work
006 How do Neural Networks learn
007 Gradient Descent
008 Stochastic Gradient Descent
009 Back propagation
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