jmugan / Modern_practical_nlp
This course covers how you can use NLP to do stuff.
Stars: ✭ 252
Labels
Projects that are alternatives of or similar to Modern practical nlp
Stanford Cs231
Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).
Stars: ✭ 249 (-1.19%)
Mutual labels: jupyter-notebook
Pysolar
Pysolar is a collection of Python libraries for simulating the irradiation of any point on earth by the sun. It includes code for extremely precise ephemeris calculations.
Stars: ✭ 249 (-1.19%)
Mutual labels: jupyter-notebook
Cvpr2019 pyramid Feature Attention Network For Saliency Detection
code and model of Pyramid Feature Selective Network for Saliency detection
Stars: ✭ 249 (-1.19%)
Mutual labels: jupyter-notebook
Mixup Generator
An implementation of "mixup: Beyond Empirical Risk Minimization"
Stars: ✭ 250 (-0.79%)
Mutual labels: jupyter-notebook
Ml sagemaker studies
Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.
Stars: ✭ 249 (-1.19%)
Mutual labels: jupyter-notebook
Nodebook
Repeatable analysis plugin for Jupyter notebook
Stars: ✭ 249 (-1.19%)
Mutual labels: jupyter-notebook
Whirlwindtourofpython
The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"
Stars: ✭ 3,002 (+1091.27%)
Mutual labels: jupyter-notebook
Ai Projects
Artificial Intelligence projects, documentation and code.
Stars: ✭ 250 (-0.79%)
Mutual labels: jupyter-notebook
Deep Learning Machine Learning Stock
Stock for Deep Learning and Machine Learning
Stars: ✭ 240 (-4.76%)
Mutual labels: jupyter-notebook
Naucse.python.cz
Website with learning materials / Stránka s učebními materiály
Stars: ✭ 248 (-1.59%)
Mutual labels: jupyter-notebook
Deep Learning Book
Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
Stars: ✭ 2,705 (+973.41%)
Mutual labels: jupyter-notebook
Mixture Density Networks For Distribution And Uncertainty Estimation
A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)
Stars: ✭ 249 (-1.19%)
Mutual labels: jupyter-notebook
Pointrend Pytorch
A PyTorch implementation of PointRend: Image Segmentation as Rendering
Stars: ✭ 249 (-1.19%)
Mutual labels: jupyter-notebook
Team Learning Program
主要存储Datawhale组队学习中“编程、数据结构与算法”方向的资料。
Stars: ✭ 247 (-1.98%)
Mutual labels: jupyter-notebook
Nbdev
Create delightful python projects using Jupyter Notebooks
Stars: ✭ 3,061 (+1114.68%)
Mutual labels: jupyter-notebook
Pytorch Exercise
Practical Exercise Codes for PyTorch
Stars: ✭ 250 (-0.79%)
Mutual labels: jupyter-notebook
Modern Practical Natural Language Processing
This course will cover how you can use NLP to do stuff.
There are four videos
- Overview and Converting Text to Vectors
- For finding similar documents
- "I have this document or text, what others talk about the same stuff?"
- Video
- Learning with Vectors and Classification
- For classifying documents
- "I need to put these documents into buckets."
- Video
- Visualizing
- For seeing what document vectors look like in 3D space
- "I need to quickly see what looks similar to what."
- Video
- Sequence Generation and Extracting Pieces of Information from Text
- For translation and document summarization, and for pulling out sentences and documents that talk about specific things
- "I need every mention of a street address or business in Garland, Texas; and I need each document translated to Urdu."
- Video
Additional Details
The idea is we make short videos that focus on the aspects of NLP that currently work well and are useful.
Speech-to-text now works pretty well, so these methods will also be useful for the audio portions of videos.
All code will be available on GitHub here https://github.com/jmugan/modern_practical_nlp
About Me, Jonathan Mugan
- PhD in Computer Science in 2010 from UT Austin
- Thesis work was about how a robot could wake up in the world and figure out what is going on
- Work at DeUmbra where we build AI for the DoD
- We also work in healthcare, which I can talk about. A future video (not in this series) will cover how we use graph neural networks to identify who is at risk for opioid overdose
- Hands-on technical advisor at our sister company Pulselight
- Wrote The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion
- Advisor at KUNGFU.AI
- Also do independent consulting work
- Can find me here [email protected] or on Twitter at @jmugan
The Limits of NLP
Computers can't read
- Reading requires mapping language to internal concepts grounded in behaving in the same general environment as the writer.
- Computers don’t have those concepts.
- Example: “I pulled the wagon.” Computers don’t know that wagons can carry things or that pulling exerts a gentle tension to the arm and leg muscles as one walks.
Computers can't write
- Writing requires mapping internal concepts grounded in behaving in the same general environment as the expected reader.
- Computers don’t have those concepts
NLP Works Around Computers Not Having the Experience or Conceptual Framework to Read and Write
- NLP is about how to make natural language amenable to computation even though computers can’t read or write.
- Representing text as vectors has transformed NLP in the last 10 years.
- There are also symbolic methods that are practically useful; we will cover those too.
Additional Information on NLP, AI, and Their Limits and Promise
- Microsoft Flight Simulator 2020 is an inflection point for virtual worlds and our own
- Generating Natural-Language Text with Neural Networks
- Why Is There Life? and What Does It Have to Do with AI?
- Chatbots: Theory and Practice
- You and Your Bot: A New Kind of Lifelong Relationship
- Computers Could Understand Natural Language Using Simulated Physics
- The Two Paths from Natural Language Processing to Artificial Intelligence
- DeepGrammar: Grammar Checking Using Deep Learning
- Deep Learning for Natural Language Processing
- What Deep Learning Really Means
- My O'Reilly course on NLP: Natural Language Text Processing with Python
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].