Introduction
This roadmap explains the main steps for learning multimodal Machine Learning. The goal is simple progress from Python to multimodal models. Next, you discover how to work with vision, text, and audio in one pipeline.
هاد ال roadmap كتعطيك طريق واضح باش تتعلم multimodal ML ب خطوات بسيطة. غادي تمشي من Python حتى النماذج لي كاتجمع الصور والنص والصوت.
1. Learn Python
Begin with Python basics. Work with data structures. Write scripts that load images, text, or audio files.
Read: Python Basics for AI
Key Skills
- Lists and dictionaries
- Functions
- Modules
- File handling
- String processing
تعلم Python مزيان. خدم ب lists o dictionaries و دير سكريبتات كيقراو images و text و audio.
2. Build Math Foundations
Multimodal models need simple math. Focus on linear algebra, probability, and statistics.
Core Topics
- Vectors and matrices
- Dot product
- Distributions
- Variance and standard deviation
تعلم math الأساسية بحال matrices و distributions و variance.
3. Learn Single Modality ML First
Before multimodal learning, understand each modality alone. This builds strong intuition.
Vision Basics
- Image preprocessing
- CNNs
- Classification tasks
Text Basics
- Tokenization
- Embeddings
- Transformers
Related: Transformers in AI
Audio Basics
- Spectrograms
- MFCC features
- Basic audio classification
خصك تفهم كل modality بوحدها. vision ب CNN. text ب embeddings. audio ب spectrogram.
4. Learn Multimodal Foundations
Study how models combine multiple data types. Understand feature alignment and fusion methods.
Fusion Types
- Early fusion
- Late fusion
- Joint fusion
Core Concepts
- Cross modal attention
- Shared embedding space
- Contrastive learning
تعلم fusion و shared embeddings و cross modal attention.
5. Study Multimodal Architectures
Learn models built for combining modalities. Study how they handle text, images, and audio in one system.
Important Models
- CLIP
- ViLT
- LXMERT
- Vision Transformers with text encoders
- Audio transformers
تعرف على CLIP و ViLT و LXMERT وكيفاش كيعالجو بزاف ديال modalities.
6. Learn Frameworks
Use frameworks that support multimodal learning. They reduce code and improve workflow.
Useful Libraries
- PyTorch
- TensorFlow
- Hugging Face Transformers
- OpenCLIP
- TorchAudio
- TorchVision
استعمل PyTorch و TorchVision و Hugging Face ف المشاريع.
7. Practice With Multimodal Data
Work with datasets that combine text and images or text and audio. Build small pipelines.
Popular Datasets
- MS COCO
- Flickr30k
- VQA datasets
- AudioCaps
جرب datasets فيها text o image بحال COCO و Flickr30k.
8. Build Real Multimodal Projects
Train models on real multimodal tasks. Test outputs. Improve accuracy.
Project Ideas
- Text image retrieval
- Image captioning
- Visual question answering
- Audio classification with text labels
- Multimodal sentiment analysis
دير مشاريع بحال captioning و VQA باش تطبق المفاهيم.
9. Learn Evaluation and Deployment
Study evaluation metrics. Export models. Build APIs. Test inference speed.
Evaluation Metrics
- Recall
- Accuracy
- F1 score
- BLEU for captioning
- CIDEr for captioning
قيم الموديل ب F1 و Recall و BLEU ف الترجمة و captioning.
Syntax or Model Structure Example
Below is a simple example showing how to load an image and text together in PyTorch.
from PIL import Image
import torch
from torchvision import transforms
image = Image.open("image.jpg")
text = "A small cat on the table"
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
img_tensor = transform(image)
print(img_tensor.shape)
print(text)
هادا مثال بسيط باش تحمل image و text ف نفس السكريبت.
Exercises
- Load an image and convert it to a tensor.
- Create a spectrogram from any audio file.
- Tokenize a short text with a transformer tokenizer.
- Implement early fusion for image and text features.
- Compute cosine similarity between embeddings.
- Train a CNN on a small image dataset.
- Train a text classifier with embeddings.
- Load a CLIP model and test image text matching.
- Evaluate a captioning model with BLEU.
- Deploy a multimodal model using a small API.
Conclusion
Follow the steps and keep training models. Multimodal learning grows with consistent practice and real projects.
تبع الخطوات و خدم على مشاريع مختلفة باش تولي قوي ف multimodal ML.