Q3 2025 Action Plan
Summer 2025 Portfolio Update
Phase 1: Foundation
Update Action Plans UI
Create task lists Q3 2025
Migrate stories to client-rendered format (e.g. dynamic import)
Write and add new stories
Categorize projects: frontend, backend, full sites, tools, web & mobile apps
Redesign project section with filtering and search
Phase 2: Content & Credentials
Add all published research with title, abstract, link, and thumbnail
Add diploma with PDF/image and badge if possible
Design CV page and downloadable PDF
Highlight CV sections: education, experience, projects, research, skills
Phase 3: Showcase & Expansion
Create startup landing page: problem, solution, features, stack, roadmap
Design the page for investors and collaborators (clean and persuasive)
Phase 4: Iteration & Polishing
Review performance (Core Web Vitals, lazy loading, images)
Optimize SEO (meta tags, structured data for projects/stories)
Add optional blog or insight section (tech tips, reflections)
Polish UI: micro animations, dark mode, responsive UX
Deep Learning - D2L.ai Book
Preface & Setup
Read Preface
Installation
Understand Notation
1. Introduction
Introduction
2. Preliminaries
Data Manipulation
Data Preprocessing
Linear Algebra
Calculus
Automatic Differentiation
Probability and Statistics
3. Linear Neural Networks for Regression
Linear Regression
Object-Oriented Design for Implementation
Synthetic Regression Data
Implementation from Scratch
Concise Implementation
Generalization
Weight Decay
4. Linear Neural Networks for Classification
Softmax Regression
Image Classification Dataset
Base Classification Model
Implementation from Scratch
Concise Implementation
Generalization in Classification
Environment and Distribution Shift
5. Multilayer Perceptrons
MLP Basics
Implementation
Forward & Backward Propagation
Numerical Stability & Initialization
Generalization in Deep Learning
Dropout
Predicting House Prices on Kaggle
6. Builders' Guide
Layers and Modules
Parameter Management
Parameter Initialization
Lazy Initialization
Custom Layers
File I/O
GPUs
7. Convolutional Neural Networks
From Fully Connected to Convolutions
Convolutions for Images
Padding and Stride
Multiple Input & Output Channels
Pooling
LeNet
8. Modern CNNs
AlexNet
VGG
Network in Network (NiN)
GoogLeNet
Batch Normalization
ResNet & ResNeXt
DenseNet
Designing CNN Architectures
9. Recurrent Neural Networks
Working with Sequences
Converting Raw Text
Language Models
RNN Basics
RNN Implementation from Scratch
Concise RNN Implementation
Backpropagation Through Time
10. Modern RNNs
LSTM
GRU
Deep RNNs
Bidirectional RNNs
Machine Translation Dataset
Encoder-Decoder Architecture
Seq2Seq Learning
Beam Search
11. Attention Mechanisms & Transformers
Queries, Keys, and Values
Attention Pooling
Attention Scoring Functions
Bahdanau Attention
Multi-Head Attention
Self-Attention & Positional Encoding
Transformer Architecture
Vision Transformers
Large-Scale Pretraining with Transformers