lukmanaj/arewads-deep-learning

Arewa Data Science Deep Learning Curricula

ArewaDS website: https://arewadatascience.github.io

Table of Contents (Arewa DL)

• Arewa Deep Learning with Pytorch Curriculum!
  • Objectives:
  • Interested in Joining the Fellowship?
  • Contact & Community:
  • Welcome to Cohort 1.0 ArewaDS Deep Learning with PyTorch Fellowship
    • Fellowship Structure
  • Graduation Requirements
  • Fellowship Kickoff
  • Prerequisites
  • Curriculum Breakdown
    • Part 1: Deep Learning with PyTorch
    • Part 2: Natural Language Processing with PyTorch
    • References

Arewa Deep Learning with Pytorch Curriculum!

Welcome to the Deep Learning Course at Arewa Data Science Academy! This comprehensive course introduces you to deep learning, focusing on PyTorch, Natural Language Processing (NLP), and Computer Vision. Combining in-depth theoretical concepts with practical application, it's tailored for beginners and those looking to enhance their knowledge in AI.

Objectives:

• Master deep learning principles through PyTorch.
• Gain hands-on experience in NLP and Computer Vision.
• Develop a portfolio of real-world deep learning projects.

Interested in Joining the Fellowship?

Application for Deep Learning Cohort 1.0 is closed, but you're welcome to join our sessions and access materials for self-study. Stay updated on future cohorts via our social media and Telegram group.

Contact & Community:

• Website: Arewa Data Science Official Website
• Email: [email protected]
• Twitter | Facebook | LinkedIn | YouTube | Telegram

Welcome to Cohort 1.0 ArewaDS Deep Learning with PyTorch Fellowship

Welcome to ArewaDS Deep Learning with Pytorch Cohort 1.0! Our fellowship offers a structured path to mastering both fundamentals and advanced concepts in deep learning.

Fellowship Structure

• Stage 1: Deep Learning with Pytorch - Fundamental and advanced topics in Deep Learning using PyTorch.
• Stage 2: Natural Language Processing with Deep Learning - Advanced NLP techniques and applications.

Graduation Requirements

To graduate, fellows must:

• Complete all curriculum modules.
• Submit all assignments and blog posts.
• Maintain a 90% attendance rate.
• Complete a capstone project approved by the ArewaDS Team.

Fellowship Kickoff

Find below the resources for the kickoff of the fellowship.

Component	Resource
Accepted Fellows	Accepted Fellows Page
Communication (Telegram)	Telegram Group Guide
Kickoff Recording	Kickoff Recording
Kickoff Slides	Kickoff Slides

Prerequisites

• Basic Python programming skills.
• Fundamental understanding of machine learning concepts.

Curriculum Breakdown

Part 1: Deep Learning with PyTorch

Explore deep learning fundamentals using PyTorch, a leading framework for deep learning.

• Resource: Deep Learning with PyTorch
• Topics Covered:
  • PyTorch Basics
  • Neural Networks
  • CNNs, RNNs
  • Advanced Topics: GANs, Reinforcement Learning

Date	Lesson	Exercise	Recordings
Week 0	Introduction		Introductory Video
Week 1	PyTorch Fundamentals	Exercise 1	Pytorch Fundamentals
Week 2	PyTorch Workflow	Exercise 2	PyTorch Workflow PyTorch Workflow - Q&A
Week 3	PyTorch Neural Network Classification	Exercise 3	PyTorch Neural Network Classification PyTorch Neural Network Classification - Q&A
Week 4	PyTorch Computer Vision	Exercise 4	PyTorch Computer Vision
Week 5	PyTorch Custom Datasets PyTorch Going Modular	Exercise 5	PyTorch Custom Datasets PyTorch Going Modular
Week 6	PyTorch Transfer Learning		PyTorch Transfer Learning
Week 7	PyTorch Experiment Tracking Capstone Project Introduction	Capstone Project	PyTorch Experiment Tracking and Capstone Project
Week 8	PyTorch Paper Replicating	Capstone Project
Week 9	PyTorch Model Deployment	Capstone Project

Part 2: Natural Language Processing with PyTorch

Delve into NLP using PyTorch, guided by the Stanford course, CS224N.

• Resource: Stanford CS224N
• Topics Covered:
  • Text Processing
  • Word Vectors
  • Neural Networks for NLP
  • Language Models
  • Applications in Sentiment Analysis and Machine Translation
• Projects:
  • Sentiment Analysis Model
  • Neural Machine Translation System
  • Chatbot Development

References

• Deep Learning with PyTorch
• Understanding Deep Learning by Simon Prince