Deep Learning for NLP with PyTorch (Cohort 2)

Mar 03, 11:00AM PST(19:00 GMT) | Wed,Fri
Description
Syllabus
Instructors
Student Reviews
COURSE OBJECTIVES:
Natural Language Processing (NLP) is the fastest-growing field of deep learning with interest and funding from top AI companies to solve problems of language, text, and unstructured information. This has resulted in a tremendous focus on model building that combines language, mathematics, and computer science.
This 4-weeks course will focus on problems of text summarization, question answering, and sentiment classification using modern approaches to model-building (GNMT, BERT, and GPT2). We will apply this to real-world problems to create an NLP pipeline on top of the PyTorch framework and spaCy.
The course offers both theoretical and practical, lab-heavy modules. By completing you would be able to:
  • Have working knowledge of PyTorch to train your own deep learning models.
  • Use OpenVINO to run model optimizer
  • Use less compute and memory for deploying model inference in production.
  • Build end to end NLP pipeline with everything you learn
  • This course is packed with practical exercises and code labs.
    * We meet twice a week at online classroom (powered by zoom)
    * Practical walkthroughs that present solutions to actual, real-world problems and challenges
    * A no-nonsense teaching style that cuts through all the cruft and help you master NLP and Pytorch

    COURSE SCHEDULE:
    • Session 1: Mar 3, 11am-1pm PST (US Pacific Timezone, GMT-8)
    • Session 2: Mar 5, 11am-1pm PST
    • Session 3: Mar 10, 11am-1pm PST
    • Session 4: Mar 12, 11am-1pm PST
    • Session 5: Mar 17, 11am-1pm PDT (US Pacific Timezone, Daylight Saving Time, GMT-7)
    • Session 6: Mar 19, 11am-1pm PDT
    • Session 7: Mar 24, 11am-1pm PDT
    • Session 8: Mar 26, 11am-1pm PDT

    COURSE INCLUDE:
    • 4 weeks / 8 sessions / 16 hours
    • 8 lectures / 4 hands-on code labs/Capstone project
    • Live session (with zoom) and real time interaction
    • Slack support during/after class

    COURSE CONTENT:
    Check the Syllabus tab for full course content.

    WHO SHOULD LEARN:
    Developers, engineers, data scientists

    PREREQUISITE:
    Python coding skills, intro to PyTorch framework is helpful, ML basics, Use Jupyter notebook on a chrome browser

    Difficulty Level
    Beginner~Intermediate
    Est. time spend per week: 4 hours live class (required) + 2 hours homework/capstone project (optional)

    REFUND POLICY
    Full refund upon request before the first session ends (Mar 3rd, 2021 1pm PST). 5% transaction fee is not refundable.

    SESSION REPLAY
    If missed live sessions, you can watch recordings any time, along with interactive learning tools, slides, course notes
    Students have one year access to course materials

    BENEFITS
  • Earn Certificate of Completion
  • Scholarship is available for application, contact for details
  • PREVIOUS COHORTS:
  • Cohort 1: Dec. 12, 2020
  • Module 1: NLP Fundamentals
    Learning Objectives: This session will overview NLP tenqiues, transfer learning, and introduce the fundamentals and application of Language Modeling Tools. Your will also learn use NLP pipeline to process documents, Word Vectors
    • Fundamentals and application of Language Modeling
    • Classical vs Deep Learning NLP
    • NLP Pipeline
    • Use NLP pipeline to process documents
    • POS, Word embedding
    • Lab 1 & 2

    Module 2: NLP Libraries and Pytorch
    Learning Objectives: This session will introduction to key packages and libraries of NLP, and get staryed with SpaCy and Pytorch
    • Key packages & libraries in NLP
    • Dive into SpaCy
    • Intro Pytorch
    • Lab 3 & 4

    Module 3: Deep Learning for NLP
    Learning Objectives: This session will discuss a few important and commonly used NLP techniques and algorithms with deep learning.
    • RNN, LSTM with PyTorch
    • Using Seq2Seq model for machine translation
    • Lab 5

    Module 4: Deep Learning for NLP (continued)
    Learning Objectives: This session will discuss a few important and commonly used NLP techniques and algorithms with deep learning.
    • Text Classification
    • Text Summarization
    • Lab 6: LSTM based text classifier
    • Lab 7: TFIDF and Logistic Regression based classifier
    • Lab 8: Multi-label classifier
    • Lab 9: Text Summarization
    • Capsone project assigment and discussion

    Module 5: Transfer Learning and Transformers
    Learning Objectives: This session will deep dive into transformer architecture.
    • Introduction to Transformers
    • Paper review (Attention is All you Need)
    • Transfer Learning Fundamentals
    • Pre-trained models, such as BERT, XLNet from Huggingface
    • Lab 10: Solve NLP problems using PyTorch, pre-trained models

    Module 6: Question/Answering with Chatbot
    Learning Objectives: This session will discuss Question / Answering through developing a chatbot.
    • Overview and theory
    • Stanford Question Answering Dataset (SQuAD)
    • Lab 11: Develop a chatbot

    Module 7: NLP Pipelines
    Learning Objective: This session will discuss MLOps using a text classification model
    • Scheduler Overview
    • Implementation walk-through
    • Lab 12

    Module 8: NLP in production
    Learning Objective: This session will discuss implement NLP in production system
    • NLP in production
    • capstone project demo
    Yashesh Shroff, Ravi Ilango

    Ravi Ilango
    Sr. Data Scientist working on a variety of revenue-generating projects for clients involving machine learning and deep learning. He worked as Sr Data Scientist at Apple for 10 years, and a Sr Program Manager at Applied Materials

    Yashesh Shroff,PhD
    Lead AI of Intel, where he focuses on enabling the AI ecosystem on heterogeneous compute. He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley

    Average
    4.5
    (41 Ratings)

    Student Feedback

        85%
        11%
        4%

    The instructors did a great job explain the subject.(Saeid H. from Class 20201212)

    I think that more should habeen pre-populated on the course page. I was switching between my work and personal computer and colab and jupyter since I wanted to know i could retain after the course. It was hard to get links from zoom then slack then github and I had zoom problems with my computer so I was following zoom on an iPad. It ended up being a distraction. Overall content is a great base and I like going from basics to library approach. I am new to NLP so it was a firehose. I think it was just destined to be that way but I picked up a lot and will take it further.(Karna B. from Class 20201212)

    Both instructors were great. I like the delivery style of Yash. Very practical oriented.(Chandan D. from Class 20201212)

    Working code examples and code walk-throughs. (Andrew B. from Class 20201212)

    This course had a nice planned structure and the content seemed interesting. However, I felt that one of the instructors wasnot that prepared and this made his part of the talk a bit confusing, even though I could tell that he had a lot of experience and knowledge.. (Laura S. from Class 20201212)

    Yash and Ravi were really good instructors, and I appreciate all the sample jupyter notebooks which I can use as starting points for actual projects we currently have.. (Harry S. from Class 20201212)

    This was an excellent workshop, instructors were well-prepared, provided lots of code samples we tried during the workshop, are obviously experts in the field, and came with real-world knowledge.. (Harry S. from Class 20201212)

    Working through the variety of application and use cases.(shaun p. from Class 20201212)

    • Start Date: Mar 03, 11:00PST | Wed,Fri
    • Venue: Online (zoom)
    • Fee:
      $299 $299 USD
    • Status: live now
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