Dive into Deep Learning
without prior AI/ML experience & with a qualified mentor
We provide a "portal" into a network of deep learning specialists for motivated undergraduate students who are brand new to AI/ML,
welcoming disadvantaged students from all backgrounds.
ABOUT

About

If you are a college student who has taken computer science courses and know how to program in Python, and now you want to learn about AI/ML but don't have any prior experience and don't know where to begin — then the Deep Learning Portal program is for you!

Our goal is to help students ramp up quickly in deep learning without having to tackle loads of prerequisite materials beforehand, which can be intimidating and pose a high barrier to entry. Students dive straight into deep learning with close guidance from one of our qualified mentors.

We welcome applicants from all backgrounds, including but not limited to low-income students, first-generation students, and students who have overcome unique personal hardships in life. Our application provides an opportunity for students to tell us about their own unique story.

The Deep Learning Portal program consists of three core components:

Course Assistance

Our qualified mentors closely guide students through a foundational deep learning course (Deep Learning Specialization on Coursera), helping to fill knowledge gaps on the fly and assisting with course concepts.

General Advising

We host informal fireside chats with special guests and hold open AMA-style office hours for students seeking general guidance on their academic career in deep learning and beyond.

Deep Learning Network

Our program connects students with competent deep learning specialists and establishes a community of motivated fellow students from a diverse range of backgrounds.

Team

Click each individual's photo for more information.

Moo Jin Kim

Founder, Director, Mentor

Stanford CS PhD Student

Chelsea Finn

Faculty Advisor

Stanford CS/EE Assistant Professor

Percy Liang

Faculty Advisor

Stanford CS Associate Professor

Breauna Spencer

Program Advisor

Stanford CS Director of DEI

Ahmed Ahmed

Strategic Advisor

Stanford CS PhD Student

Anikait Singh

Program Coordinator, Mentor

Stanford CS PhD Student

Irena Gao

Student Liaison, Mentor

Stanford CS PhD Student

Steven Cao

Mentor

Stanford CS PhD Student

Maximilian Du

Mentor

Stanford CS Undergrad

Swastika Dutta

Mentor

Stanford CS MS Graduate

Tian Gao

Mentor

Stanford CS PhD Student

Rohith Kuditipudi

Mentor

Stanford CS PhD Student

Alec Lessing

Mentor

Stanford CS MS Student

Lisa Li

Mentor

Stanford CS PhD Student

Ken Liu

Mentor

Stanford CS PhD Student

Max Sobol Mark

Mentor

Stanford CS MS Student

Jonathan Yang

Mentor

Stanford CS PhD Student

Program Timeline

Key events in the program are shown below.

  • Student Applications Open

    Students from select universities apply to our program, submitting application materials such as resume/CV, transcript, background questionnaire, and personal statement.

  • Review of Applications &
    Student-Mentor Matching

    Students who are accepted into the program are matched with one of the mentors, who will serve as the primary contact throughout the program.

  • Deep Learning Coursework &
    Program Activities

    Students complete a foundational deep learning course with close guidance from mentors. We also host activities, such as fireside chats, open office hours, and guest talks.

  • Program Completion

    Students are suggested next steps to continue their careers in deep learning after the program. Fellow students optionally stay in contact past program completion through a close-knit alumni network.

Apply

The application form for the Spring 2024 iteration of Deep Learning Portal has closed.

Spring 2024 Schedule

Below is the week-by-week schedule for the deep learning curriculum (Deep Learning Specialization on Coursera).

Course modules and assignments listed under each week should be completed by the start date of that week.

Each student can choose to complete either the Computer Vision Track or the Natural Language Processing Track.

Weekly group session topics for students are also listed below and will be updated throughout the spring quarter.

Week Start Date Course Modules Course Assignments Weekly Group Sessions
Week 1 4/1/24 None None Program kick-off & introductions
Week 2 4/8/24 • C1M1: Introduction to deep learning
• C1M2: Neural Network Basics 		• Python Basics with Numpy
• Logistic Regression with a Neural Network Mindset 		Group office hours (deep learning curriculum)
Week 3 4/15/24 • C1M3: Shallow Neural Networks
• C1M4: Deep Neural Networks 		• Planar Data Classification with One Hidden Layer
• Building your Deep Neural Network: Step by Step
• Deep Neural Network - Application 		Ask Me Anything with Moo Jin Kim (Deep Learning Portal founder)
Week 4 4/22/24 None None --
Week 5 4/29/24 • C2M1: Practical Aspects of Deep Learning
• C2M2: Optimization Algorithms 		• Initialization
• Regularization
• Gradient Checking
• Optimization Methods 		"Struggling to Fail Upwards" — presentation by Ahmed Ahmed (Stanford CS PhD student and Deep Learning Portal advisor)
Week 6 5/6/24 • C2M3: Hyperparameter Tuning, Batch Normalization and Programming Frameworks • TensorFlow Introduction Casual Q&A with Samuel Kwong (Stanford CS BS/MS graduate & ML Software Engineer at Waymo)
Week 7 5/13/24
Computer Vision Track
• C4M1: Foundations of Convolutional Neural Networks
• C4M2: Deep Convolutional Models: Case Studies

---- OR ----

Natural Language Processing Track
• C5M1: Recurrent Neural Networks
Computer Vision Track
• Convolutional Model, Step by Step
• Convolution Model Application
• Residual Networks
• Transfer Learning with MobileNet

---- OR ----

Natural Language Processing Track
• Building a Recurrent Neural Network - Step by Step
• Dinosaur Island-Character-Level Language Modeling
• Jazz Improvisation with LSTM
Casual Q&A with Christopher Wolff (Stanford CS MS graduate & AI Research Engineer at Google DeepMind)
Week 8 5/20/24 None None --
Week 9 5/27/24
Computer Vision Track
• C4M3: Object Detection

---- OR ----

Natural Language Processing Track
• C5M2: Natural Language Processing & Word Embeddings
Computer Vision Track
• Car Detection with YOLO
• Image Segmentation with U-Net

---- OR ----

Natural Language Processing Track
• Operations on Word Vectors - Debiasing
• Emojify
Casual Q&A with Eric Anthony Mitchell (Stanford CS PhD student in AI & NLP)
Week 10 6/3/24
Computer Vision Track
• C4M4: Special Applications: Face Recognition & Neural Style Transfer

---- OR ----

Natural Language Processing Track
• C5M3: Sequence Models & Attention Mechanism
Computer Vision Track
• Art Generation with Neural Style Transfer
• Face Recognition

---- OR ----

Natural Language Processing Track
• Neural Machine Translation
• Trigger Word Detection
Casual Q&A with Percy Liang (Stanford ML/NLP professor & director of CRFM: Center for Research on Foundation Models)
Final Week 6/10/24
Computer Vision Track
None

---- OR ----

Natural Language Processing Track
• C5M4: Transformer Network
Computer Vision Track
None

---- OR ----

Natural Language Processing Track
• Transformers Architecture with TensorFlow
• Transformer Pre-processing
• Transformer Network Application: Named-Entity Recognition
• Transformer Network Application: Question Answering
TBA

Contact Us

Have any questions? Feel free to email us at deeplearningportal at gmail dot com.