Adjust the Weekly Plan with LLMs

Problems with the previous plan

Although the plan was good. But while I’m trying to execute it, I’ve found several realistic problems.

• Lack of Math Knowledge(Linear Algebra, Calculus)
  so it was hard to understand the formula and explanation.
• Different suggestions
  I tried several LLMs(ChatGPT, Claude, Grok, Deepseek), but of course all of them have gave me the different answers.
  But for me, An Undergraduate Student, it was hard to select and modify the plan they gave me.
• Different Focus
  The LLMs have gave me very different answer when I change the terms. (e.g efficient and fast -> efficient)
  Even the focus were changing(too shallow, too many resources, out of scope)
  So I had to fixed my requirements and conditions.

New method

1. Ask again with Format, Role, Focusing point, Requirement, My situasion
   So I rewrote the prompt mentioning some of their answer.

    As a professional AI researcher. imagine you're making it for a student do entire questions
    [former CSV table format including data]
    keeping
    Advanced Focus: Resources go beyond undergraduate basics (e.g., introductory Python or linear algebra) and dive into theoretical and practical depths of ML/DL.
    Research-Oriented: Includes seminal papers and recent works to align with your goal of following cutting-edge research.
    Master’s Preparation: Structured to help you explore specializations (e.g., NLP, computer vision) and build a strong application profile.
    Evidence-Based: Recommendations are grounded in widely recognized materials used in academia and industry.
    [my-plan-period]
    [my-time-table]
   

   And I used the function (deep think/reason/… anyway)

2. Let them judgement their responses
   I also requested to make CSV table of the plan.
   And then copied All of them and ask again with the function ```
   1. [CSV1]
   2. [CSV2]
   3. [CSV3] which is the best of all? and which is the best of only study? ``` I asked “only study” because their answer contained community, forum and Master preparation

Got the Satisfactory grain

for my situation, it was Deepseek>ChatGPT>Grok>Claude
also I was very amazed of Deepseek’s analytical answers. The quality was pretty high.
So, after some of formatting and adjustment. it is done!
And I don’t think I would change the plan even if I didn’t complete it, I would just re-schedule to finish them.

Math Plan Table

Focus Area	Topics	Resources	Tasks	Deliverables	Research Links
Vector Spaces	Vector spaces, subspaces, linear combinations	Friedberg Ch. 1.1-1.3; 3Blue1Brown Video 1-3	Watch 3Blue1Brown videos (1 hr)	Notes on vector axioms	-
Vector Spaces	Vector spaces, subspaces, linear combinations	Friedberg Ch. 1.1-1.3; 3Blue1Brown Video 1-3	Read Friedberg Ch. 1.1-1.3 (2 hrs); Solve Friedberg Ex. 1.4 (2 hrs); Python code for linear combinations (2 hrs)	Python code for linear combinations	-
Linear Transformations	Linear maps, matrix representations	Friedberg Ch. 2.1-2.3; MML Ch. 2.7	Read Friedberg Ch. 2.1-2.3 (3 hrs); Implement matrix multiplication in NumPy (2 hrs); Proof of linearity for a map (2 hrs)	Code for matrix transformations; Proof of linearity for a map	-
Bases & Dimension	Linear independence, bases, dimension	Friedberg Ch. 1.5-1.6; MML Ch. 2.5-2.6	Read Friedberg Ch. 1.5-1.6 (3 hrs); Solve MML Ex. 2.5 (1 hr); Basis for a dataset (e.g., MNIST pixels) (2 hrs)	Basis for a dataset; Dimension analysis notes	-
Eigenvalues	Eigenvalues, eigenvectors, diagonalization	Friedberg Ch. 5.1; MML Ch. 4.2	Read Friedberg Ch. 5.1 (2 hrs); Compute eigenvalues for 3x3 matrices (2 hrs); Notes on PCA motivation (1 hr)	Eigenvalue Python script; Notes on PCA motivation	Turk & Pentland (1991) - Eigenfaces
SVD & Inner Products	Inner product spaces, Gram-Schmidt, SVD	Friedberg Ch. 6.1-6.2, 6.7; MML Ch. 4.5	Implement SVD in NumPy (3 hrs); Read MML Ch. 4.5 (2 hrs); Truncated SVD for image compression (1 hr)	Truncated SVD for image compression; Orthogonal basis code	Koren et al. (2009) - SVD in Recommender Systems
Probability Basics	Probability spaces, distributions	MML Ch. 6.1-6.2; Deep Learning Ch. 3.9	Read MML Ch. 6.1-6.2 (2 hrs)	Gaussian sampling Python script; Probability axioms summary	-
Review & PCA	Review concepts, PCA introduction	MML Ch. 10.2; Friedberg Ch. 6.7	Summarize week (1 hr)	1-page PCA summary	-
Advanced SVD	SVD theory, low-rank approximations	Golub & Van Loan Ch. 2.4; MML Ch. 4.6	Read Golub Ch. 2.4 (3 hrs); Optimize SVD code for speed (2 hrs); Benchmarked SVD code (1 hr)	Benchmarked SVD code; Notes on numerical stability	-
Gradients & Backprop	Vector calculus, chain rule	MML Ch. 5.2-5.6; CS231n Notes	Derive gradients for a neural network (4 hrs); Verify with PyTorch (1 hr); Manual gradient calculations (2 hrs)	Manual gradient calculations; Autograd verification script	-
Optimization	Gradient descent, Lagrange multipliers	Boyd & Vandenberghe Ch. 9; MML Ch. 7.1-7.3	Code gradient descent for linear regression (3 hrs); Read Boyd Ch. 9 (2 hrs); Convergence plot (1 hr)	Convergence plot; Lagrange multiplier examples	Kingma & Ba (2017) - Adam Optimizer
Convexity	Convex sets, functions, optimization	Boyd & Vandenberghe Ch. 2-3	Prove convexity for SVM loss (2 hrs); Read Boyd Ch. 2 (3 hrs)	Convexity proofs; Notes on SVM objective	-
Probability Deep Dive	Exponential family, MLE	Deep Learning Ch. 3.9-3.12; MML Ch. 6.6	Implement MLE for Gaussian (2 hrs); Read DL Ch. 3.9 (3 hrs); Exponential family summary (1 hr)	MLE Python script; Exponential family summary	-
NLP Math I	Word embeddings, attention	Attention Is All You Need (Vaswani et al., 2017)	Code self-attention with NumPy (2 hrs)	Transformer attention layer	Vaswani et al. (2017) - Transformers
NLP Math II	Geometric intuition, manifolds	Word2Vec (Mikolov et al., 2013); MML Ch. 3	Visualize word vectors with t-SNE (1 hr)	t-SNE visualization code	Mikolov et al. (2013) - Word2Vec
CV Math I	Convolutions, CNNs	ImageNet Classification (Krizhevsky et al., 2012)	Implement Conv2D with NumPy (4 hrs); Read Sec. 2 (1 hr); Receptive field analysis (1 hr)	Conv layer code; Receptive field analysis	Krizhevsky et al. (2012) - AlexNet
CV Math II	BatchNorm, gradient flow	Batch Normalization (Ioffe & Szegedy, 2015)	Derive BatchNorm gradients (3 hrs); Read Sec. 4.2 (2 hrs); Gradient flow notes (2 hrs)	BatchNorm code; Gradient flow notes	Ioffe & Szegedy (2015) - BatchNorm
Research Paper I	Reproduce a result	Choose paper (e.g., PCA, Adam)	Replicate PCA on CIFAR-10 (6 hrs)	Jupyter notebook; Comparative analysis	User-selected paper
Advanced Proofs	Spectral theorem, Jordan form	Friedberg Ch. 6.7, 7.1	Study Friedberg proofs (4 hrs); Summarize (1 hr)	Proof summaries; Applications to kernels	-
Measure Theory	Probability spaces, Lebesgue integral	Royden Ch. 2-3	Read Royden Ch. 2 (3 hrs); Solve Ex. 2.5 (2 hrs); Sigma-algebra examples (1 hr)	Measure theory notes; Sigma-algebra examples	-
Specialization Elective	Choose: NLP (BERT) or CV (ResNet)	BERT (Devlin et al., 2018) / ResNet (He et al., 2015)	Implement BERT embedding or ResNet block (2 hrs)	Code + performance report	Devlin et al. (2018) or He et al. (2015)
Portfolio Prep	GitHub, summary document	-	Organize code/docs (1 hr)	GitHub repo	-
Portfolio Prep	GitHub, summary document	-	Write READMEs (2 hrs); Organize code/docs (2 hrs); 1-page research summary (2 hrs)	1-page research summary	-

ML/DL Plan Table

Focus Area	Learning Objectives	Resources (Books/Papers)	Courses/Video Lectures	Hands-on Tasks	Community/Research Engagement	Deliverables/Outputs
Supervised Learning & Optimization	Master ML fundamentals: loss functions, gradients, SGD	PRML (Ch. 1-3); SGD paper (Robbins & Monro, 1951)	CS229 (Supervised Learning)	Implement logistic regression from scratch	Join ML forums (e.g., Reddit r/MachineLearning)	Code + report comparing SGD variants
Advanced Linear Algebra & Matrix Computations	Explore SVD, eigen-decomposition, and their theoretical implications	Advanced sections in MML; Recent papers on matrix factorization in DL	YouTube seminars by 3Blue1Brown (advanced topics)	Implement SVD/PCA from scratch, analyze datasets	Share findings on GitHub and forums	SVD/PCA implementation with analysis report
Neural Network Mechanics	Understand backpropagation, activation functions	Deep Learning (Ch. 6); Backprop paper (Rumelhart et al., 1986)	MIT 6.S191 (Intro to Deep Learning)	Code a 3-layer MLP with NumPy	Engage in online study groups (Slack/Discord)	MLP implementation with ablation study
Regularization & Evaluation	Advanced model tuning: dropout, batch norm	DL Book (Ch. 7); Dropout paper (Srivastava et al., 2014)	CS231n (Training Neural Networks I)	Implement dropout/BatchNorm in PyTorch	Discuss results and adjustments on discussion boards	Trained model with regularization analysis
Convolutional Networks (CV)	Learn CNN architectures, pooling, strides	DL Book (Ch. 9); AlexNet paper (Krizhevsky et al., 2012)	CS231n (CNNs)	Build a CNN for CIFAR-10 classification	Post training metrics and troubleshooting tips online	CNN codebase + accuracy report
Residual Networks (CV)	Study skip connections, deep network training	ResNet paper (He et al., 2016)	CS231n (Advanced CNN Architectures)	Implement ResNet-18 on TinyImageNet	Collaborate with peers for design review	ResNet code + training curves
Attention Mechanisms	Explore self-attention, transformer basics	Attention Is All You Need (Vaswani et al., 2017)	CS224n (Transformers)	Code a single-head attention layer	Discuss challenges in study groups and on GitHub	Attention implementation with visualization
Optimization Deep Dive	Advanced optimizers: AdamW, LAMB	Adam paper (Kingma & Ba, 2017); Deep Learning Tuning Playbook	Fast.ai (Practical Deep Learning)	Benchmark optimizers on a vision task	Share experiment results on GitHub or forums	Optimizer comparison dashboard
VAEs & GANs	Latent variable models, adversarial training	VAE paper (Kingma & Welling, 2013); GAN paper (Goodfellow et al., 2014)	CS236 (Deep Generative Models)	Train a DCGAN on CelebA	Engage in discussions about training challenges	Generated samples + FID score report
Transformers & BERT (NLP)	Pre-training, fine-tuning strategies	BERT paper (Devlin et al., 2018); RoBERTa analysis (Liu et al., 2019)	Hugging Face NLP Course	Fine-tune BERT for sentiment analysis	Participate in online transformer discussions	Fine-tuning notebook + accuracy metrics
Diffusion Models	Score-based generative models	DDPM paper (Ho et al., 2020); DDIM paper (Song et al., 2021)	Stable Diffusion Tutorials	Implement a 1D diffusion process	Engage with the research community via Twitter/LinkedIn	Diffusion code + generated samples
Vision Transformers (NLP, CV)	Patch embeddings, hybrid architectures	ViT paper (Dosovitskiy et al., 2020); DeiT (Touvron et al., 2021)	CS231n (Vision Transformers)	Train ViT on CIFAR-100	Post initial findings on an ML blog	ViT implementation + ablation study
Paper Analysis	Critique methodology of recent SOTA paper	Select 1 NeurIPS/ICLR 2023-24 paper (e.g., LLaMA, DALL-E 3)	Paper author talks on YouTube	Reproduce key figures/tables	Post critiques on academic blogs/discussion boards	Critical analysis report
System Implementation	Replicate core model components	Official codebase (if available); Supplementary materials	Clone GitHub repositories	Refactor code for readability	Share detailed replication notes on a blog or forum	Clean reimplementation + documentation
Hyperparameter Search	Bayesian optimization, multi-objective tuning	Hyperparameter paper (Snoek et al., 2012); Optuna Docs	Weights & Biases Tutorials	Run large-scale hyperparameter sweep	Discuss results and adjustments on discussion boards	Hyperparameter analysis dashboard
Benchmarking	Compare with baselines, compute metrics	ML reproducibility checklist (Pineau et al.)	MLOps Zoomcamp (Evaluation)	Test on 2+ datasets (e.g., ImageNet-1k, COCO)	Share experiment results on GitHub or forums	Benchmark report with statistical tests
Choose Track: CV/NLP/RL	Deep dive into subfield tools/libraries	CV: Detectron2 Docs; NLP: Hugging Face; RL: Stable Baselines3	Advanced domain courses (e.g., CS234 for RL)	Build an end-to-end project (e.g., object detection pipeline)	Engage with mentors and specialized online groups	Project codebase + demo video
Novel Research Project	Formulate hypothesis, design experiments	Literature from arXiv (last 6 months)	Research group meetings (simulated via Discord)	Write preprint-style paper	Present findings in virtual meetups	Publishable manuscript + conference submission
Measure Theory	Probability spaces, Lebesgue integral	Royden Ch. 2-3	Read Royden Ch. 2 (3 hrs); Solve Ex. 2.5 (2 hrs)	Measure theory notes; Sigma-algebra examples
Specialization Elective	Choose: NLP (BERT) or CV (ResNet)	BERT (Devlin et al., 2018) / ResNet (He et al., 2015)	Implement BERT embedding or ResNet block (5 hrs)	Code + performance report
Portfolio Prep	GitHub, summary document	-	Organize code/docs (3 hrs); Write READMEs (2 hrs)	GitHub repo; 1-page research summary