This repository contains structured list for all the videos from my YouTube channel TechViz-The Data Science Guy where post videos on AI/ML. Feel free to checkout and subscribe to the channel for some awesome content. Not only that, but here I plan to regularly (monthly) update other meta details as well that are part of the YT creator's dashboard 😉 Happy Learning! 😄
- Last Updated on 13th January, 2021- Introducing PandasAI: Generative AI Python Library - Video (1.4k 👀 | 66 👍)
PandasAI is a Python library that integrates generative AI capabilities into pandas, making data frames conversational. With simply a text prompt, you can produce insights from your data frame.
- T5: Exploring Limits of Transfer Learning with Text-to-Text Transformer - Video (28k 👀 | 821 👍)
This paper from Google introduces T5 model (Text-to-Text Transfer Transformer) and releases large scale C4 corpus (~750GB) . T5 is a large neural network model that is trained in a pre-train, fine-tuned learning paradigm. In this framework, all NLP tasks are reframed into a unified text-to-text format where the input and output are always text strings. Fine-tuning of the model was done on various tasks like SQAD question answering, WMT translation, CNN/Daily mail Abstractive Summarisation, Sentiment analysis, etc
- GraphGPT: Transform Text into Knowledge Graphs with GPT-3 - Video (27k 👀 | 453 👍)
GraphGPT converts unstructured natural language into a knowledge graph. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or a transcript from a video to generate a graph visualization of entities and their relationships. A classic use case of prompt engineering.
- BART: Denoising Sequence-to-Sequence Pre-training for NLG - Video (24k 👀 | 351 👍)
The authors from Facebook AI propose a new pre-training objective for sequence models as a denoising autoencoder. It can be viewed as corrupting text with some arbitrary noise function while the Language Model is expected to denoise it.
- Automatic Text Summarization in Natural Language Processing (NLP) - Video
Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. There are two general approaches to automatic summarization: extractive and abstractive. In this playlist, you will find relevant Research Papers walkthroughs on Text Summarisation in (Natural Language Processing) NLP domain from top machine learning/linguistics conferences.
- AI/ML Research Paper Walkthrough - Video
This is a master playlist that holds detailed Research Paper Walkthroughs from the field of Machine Learning and Natural Language Processing from top-tier conferences like NeurIPS, ICML, EMNLP, ACL, etc
- Data Augmentation in Natural Language Processing (NLP) - Video
This playlist talks about Data Augmentation techniques in Natural Language Processing(NLP). Data augmentation is used to generate additional, synthetic data using the data you already have. Doing data augmentation can help you make your model generalize better and robust to noise.
- Total Videos: 114
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- India: 37.5%
- United States: 10.5%
- Germany: 1.2%
- What is the age group of viewers? Age Distribution 👨
- 18-24 years: 43.1%
- 25-34 years: 37.6%
- 35-44 years: 14.8%
- 55-64 years: 0.6%
- What is the Gender Distribution 👧 👦
- Female: 19.5%
- Male: 80.5%
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