Deep Learning: Advanced Natural Language Processing and RNNs

Natural Language Processing (NLP) with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

What Will I Learn?

• Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems)
• Build a neural machine translation system (can also be used for chatbots and question answering)
• Build a sequence-to-sequence (seq2seq) model
• Build an attention model
• Build a memory network (for question answering based on stories)
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Requirements

• Understand what deep learning is for and how it is used
• Decent Python coding skills, especially tools for data science (Numpy, Matplotlib)
• Preferable to have experience with RNNs, LSTMs, and GRUs
• Preferable to have experience with Keras
• Preferable to understand word embeddings

Target audience

• Students in machine learning, deep learning, artificial intelligence, and data science
• Professionals in machine learning, deep learning, artificial intelligence, and data science
• Anyone interested in state-of-the-art natural language processing