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Apr 23, 20258 min read

ML Learn Plan

The 1-Month Plan: A Weekly Breakdown

Week 1: Solidify Foundations & Dive into Transformers

  • Goal: Understand core Deep Learning for NLP concepts, Master PyTorch essentials for NLP, Get hands-on with basic Transformers using Hugging Face.
  • Learning Resources:
    • PyTorch: Finish learnpytorch.io. Ensure you understand Dataset, DataLoader, building custom nn.Module, the training loop, saving/loading models.
    • NLP Fundamentals: Quickly review concepts like tokenization (WordPiece, SentencePiece - important for mALBERT), embeddings (Word2Vec, GloVe - conceptual understanding), sequence models (RNN/LSTM - conceptual understanding of sequence processing). Resource: Stanford CS224n lectures ( selectively watch lectures 1-7 for foundations, maybe skip if time is tight and rely on Hugging Face course), or find good blog posts/tutorials explaining these concepts. https://youtube.com/playlist?list=PLoROMvodv4rOaMFbaqxPDoLWjDaRAdP9D
    • Transformers Introduction:
      • Must Read: Jay Alammar’s β€œThe Illustrated Transformer” - Excellent visual explanation.
      • Must Do: Start the Hugging Face Course (https://huggingface.co/learn/nlp-course). Focus on Chapters 1-4 this week. This is critical for practical implementation. It covers library usage, tokenizers, fine-tuning, and the ecosystem.
  • Practice:
    • Implement a simple text classification model in PyTorch from scratch (using nn.Embedding, maybe a simple RNN or Linear layer).
    • Replicate Hugging Face course examples: Load pretrained models/tokenizers (like bert-base-multilingual-cased which is related to mALBERT), perform inference, fine-tune a model on a standard classification/token classification dataset using the Trainer API.
    • Kaggle: Continue with Titanic to practice the general ML workflow (data loading, preprocessing, model training, evaluation). Aim to submit a result.
  • Tools: Get comfortable with Google Colab or Kaggle Kernels for free GPU access. Set up your local environment with PyTorch and Hugging Face transformers, datasets, tokenizers.
  • Ebook/Reading: Deep Learning with Python by FranΓ§ois Chollet (Good high-level intro, concepts are transferable from Keras) OR Dive into Deep Learning (d2l.ai - interactive, uses PyTorch/MXNet/TF).

Week 2: Deeper into Transformers, BERTology & mALBERT

  • Goal: Understand the architecture of BERT-like models (Attention, Encoders), pretraining objectives (MLM, NSP), and specifics of ALBERT/mALBERT. Master Hugging Face Trainer and data handling.
  • Learning Resources:
    • Transformers Deep Dive:
      • Jay Alammar’s β€œIllustrated BERT” and β€œIllustrated GPT-2”.
      • Hugging Face Course: Chapters 5-8 (Deeper dive into datasets, tokenizers, custom loops).
      • Read the original BERT paper (focus on understanding MLM and NSP).
      • Read the ALBERT paper (focus on parameter reduction techniques: embedding factorization, cross-layer parameter sharing). Understand how mALBERT adapts this for multilingual settings.
    • Russian Resources: Check OpenDataScience (ods.ai) community resources/tutorials on Transformers/BERT, they often have high-quality Russian materials.
  • Practice:
    • Fine-tune bert-base-multilingual-cased or an actual mALBERT model (check Hugging Face Hub) on a Russian NLP task (e.g., NER, POS tagging, classification). Use the datasets library to load data.
    • Experiment with different tokenizers available for multilingual models.
    • Crucially: Try writing a custom PyTorch training loop for a Transformer model (without the Trainer initially) to understand the mechanics, then compare it with using the Trainer. This helps demystify the library.
    • Start exploring the LiLT paper (https://arxiv.org/abs/2202.13669). Focus on understanding its inputs (text + bounding boxes), architecture modifications, and pretraining objectives (MIM, MLM, PA).
  • Tools: Continue using Colab/Kaggle. Familiarize yourself with TensorBoard integration in Hugging Face Trainer or your custom loop (you mentioned MLOps experience, this should be straightforward).
  • Ebook/Reading: Start Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, Thomas Wolf (Hugging Face team). This is highly relevant and practical.

Week 3: Focus on LiLT, Pretraining & Integration Strategy

  • Goal: Understand LiLT architecture and pretraining details. Set up an environment to potentially run LiLT code. Strategize the mALBERT+LiLT integration.
  • Learning Resources:
    • LiLT:
      • Deep dive into the LiLT paper and its official GitHub repository (if available) or implementations based on it (check Hugging Face Hub models for microsoft/lilt). Understand how layout information (bounding boxes) is incorporated.
      • Look for tutorials or code examples related to Document AI or multimodal (text + layout) transformers.
    • Pretraining Code: Study Hugging Face’s scripts for running language model pretraining (e.g., run_mlm.py). Understand the data collation, masking strategies, and training arguments.
    • Integration: How could LiLT and mALBERT work together?
      • Option 1 (Feature Fusion): Use mALBERT for text features and LiLT for layout-aware text features, then combine them (concatenate? attention mechanism?).
      • Option 2 (Sequential): Use mALBERT first, then feed its embeddings into LiLT? (Less likely based on LiLT’s design).
      • Option 3 (LiLT Modification): Can LiLT’s text encoder be mALBERT? This might require modifying the LiLT architecture or weights. This seems the most plausible direction for β€œintegrating with mALBERT” in the context of LiLT pretraining. Perhaps initialize LiLT’s text encoder with mALBERT weights?
  • Practice:
    • Try loading the LiLT model architecture using Hugging Face (if available) or directly from its source code.
    • Attempt to run the official LiLT pretraining script (or a fine-tuning script first) on a very small sample dataset (like the RVL-CDIP sample or similar document datasets) to understand dependencies and workflow. Don’t expect full training yet.
    • Write code snippets to load both mALBERT and LiLT models and extract features/embeddings for a sample input. Start prototyping the chosen integration strategy.
  • Tools: You might need more compute now. Consider Colab Pro, Kaggle GPU limits, or cloud VMs (AWS, GCP, Azure). Ensure DVC is set up if you start handling larger datasets for pretraining.
  • Ebook/Reading: Continue NLP with Transformers. Look up specific chapters related to multimodal or adapting architectures.

Week 4: Implementation, Pretraining (Attempt), Integration & Deployment Prep

  • Goal: Get a LiLT pretraining loop running (even on small data/few steps). Implement the basic mALBERT+LiLT integration. Revisit mobile deployment challenges for this combined model.
  • Learning Resources:
    • Focus on debugging your code.
    • PyTorch documentation for advanced features if needed (gradient accumulation, mixed-precision training - useful for large models).
    • Revisit your mALBERT quantization and Android deployment code.
  • Practice:
    • Pretraining: Debug the LiLT pretraining script. Aim to successfully run it for a few thousand steps on a relevant dataset sample (document layout datasets). This is the most challenging part. Be realistic: Full pretraining takes days/weeks on powerful hardware. Your goal is to understand and execute the process. Maybe focus on replicating the pretraining setup for LiLT using mALBERT weights as the starting point for the text encoder part, if that’s your integration strategy.
    • Integration: Implement the code that combines mALBERT and LiLT according to your strategy from Week 3. Test it with sample inputs.
    • Deployment:
      • Analyze the combined model’s size and complexity.
      • How will you export it? ONNX? TorchScript?
      • Revisit quantization. Can both parts be quantized effectively? How does LiLT’s architecture (with layout embeddings) affect quantization?
      • Create a minimal pipeline on your development machine that takes input β†’ runs mALBERT (if separate) β†’ runs LiLT β†’ produces output.
      • If time permits: Try quantizing and converting the LiLT part (or the integrated model) using the same tools you used for mALBERT (e.g., TensorFlow Lite conversion with quantization, PyTorch Mobile quantization). Test performance on a sample Android project.
  • Tools: Heavy use of GPU resources. Profiling tools to check model speed/memory. Android Studio and your mobile ML framework (TFLite/PyTorch Mobile).
  • Ebook/Reading: Specific documentation related to model optimization, quantization, and mobile deployment frameworks. Review relevant sections of NLP with Transformers.

Throughout the Month:

  • History of ML: For bedtime reading or breaks, watch documentaries (e.g., on AlphaGo), read Wikipedia articles on key figures (Hinton, LeCun, Bengio) and milestones (Perceptron, Backpropagation, CNNs, LSTMs, Attention). A classic book is β€œThe Master Algorithm” by Pedro Domingos (more general ML, less deep learning).
  • Competitions/Hackathons: Keep an eye out, but realistically, the main project will consume most of your time. If a short, relevant hackathon appears (e.g., NLP-focused), it could be a good learning burst, but don’t sacrifice core project progress. Finishing the Titanic Kaggle notebook is a good achievable goal.
  • Language: Use English resources primarily (Hugging Face course, papers, most documentation). If you find high-quality ODS.ai courses/tutorials in Russian that directly map to your goals, use them, but don’t compromise on quality. Technical English is essential in ML.
  • Code: Write clean, modular code. Use Git religiously. Add comments, especially explaining why you’re doing something.
  • Ask Questions: Use Stack Overflow, Hugging Face forums, GitHub issues, and potentially the ODS.ai community. Don’t stay stuck for too long.

Realistic Expectations & Deliverable:

  • Full LiLT Pretraining: Pretraining from scratch on a large dataset in 1 month is extremely unlikely unless you have significant existing infrastructure and expertise. Focus on understanding the setup, running it on a small scale, or fine-tuning an existing pretrained LiLT. Perhaps the goal is modifying LiLT to use pretrained mALBERT weights.
  • Integration: A working Python prototype demonstrating the integration logic is achievable.
  • Deployment: Getting the exact integrated, pretrained model perfectly quantized and running smoothly on Android within 1 month is ambitious. A realistic goal might be:
    1. Successfully run LiLT pretraining steps (understanding the process).
    2. Build a Python prototype integrating LiLT (possibly using existing weights or your small-scale pretrained version) with mALBERT.
    3. Analyze the deployment challenges and perhaps get the LiLT architecture (with random or existing weights) converted and running on Android, identifying bottlenecks.

Your prior experience deploying mALBERT is a huge asset. Leverage that knowledge for the final deployment phase. Good luck, this is a challenging but very rewarding project!

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