Introduction
This roadmap gives simple steps for beginners and students. The goal is to build deep learning skills with clear actions. Next, you move from Python to neural networks then to advanced models.
هاد ال roadmap كتعطيك طريق سهل باش تبدا ف deep learning. غادي تمشي خطوة ب خطوة حتى تبني مهاراتك.
1. Start With Python
Deep learning depends on Python. Learn syntax. Write simple scripts. Work with lists and dictionaries. Use functions. Handle files.
What to Learn
- Variables and data types
- Lists, tuples, dictionaries
- Functions
- Modules
- Error handling
بدا ب Python. تعلم data types o functions o loops ودير سكريبتات صغار.
2. Learn Math Foundations
Math supports neural networks. Learn the essential parts only. Focus on linear algebra, calculus, probability, and statistics.
Focus Points
- Vectors and matrices
- Derivatives and gradients
- Random variables
- Mean, variance, standard deviation
الرياضيات ضرورية. تعلم matrices o gradients و المفاهيم الأساسية غير.
3. Master Machine Learning Basics
Before deep learning, understand core ML concepts. Train simple models. Test accuracy and fix errors.
Key Algorithms
- Linear regression
- Logistic regression
- KNN
- Decision trees
- Random forest
فهم ML قبل DL. تعلم regression o trees و بني موديلات صغار.
4. Understand Neural Networks
Study how neural networks process data. Learn how layers pass information. Learn loss functions and optimization.
Main Concepts
- Perceptron
- Forward pass
- Backpropagation
- Gradient descent
- Overfitting and regularization
فهم perceptron o forward o backprop. هادو أساس deep learning.
5. Pick a Deep Learning Framework
Use PyTorch or TensorFlow. PyTorch is simple. Start by building small networks and training them.
What to Practice
- Build a feedforward network
- Train with different optimizers
- Plot training curves
- Save and load models
استعمل PyTorch. بني networks صغار و جرب optimizers مختلفين.
6. Learn CNNs for Vision
CNNs help with image tasks. Learn convolution layers and pooling. Train a small classifier.
Projects
- Digit classification
- Cats vs dogs
- Simple object recognition
تعلم CNN o convolution o pooling و دير classifier للصور.
7. Learn RNNs and Transformers for Text
Work with text models. Learn tokenization and embeddings. Study RNNs. Study LSTMs. Study attention and transformer blocks.
Practice Tasks
- Text classification
- Sentiment analysis
- Next word prediction
لل NLP تعلم embeddings o attention o transformers.
8. Work With Real Projects
Pick tasks from vision or text. Train models. Debug errors. Track results. Improve performance.
Project Ideas
- Traffic sign classifier
- Spam detector
- Speech command recognizer
خدم على مشاريع حقيقية باش تزيد مهاراتك.
9. Learn Deployment
Export models. Use ONNX. Use TorchScript. Build APIs. Test performance on CPU and GPU.
دير deployment باستعمال ONNX ولا TorchScript و دير API بسيط.
10. Build Your Portfolio
Show your projects. Write simple explanations. Publish code. Share results online.
دير portfolio فيه مشاريع واضحة و code منظم.
Syntax or Model Structure Example
Below is a simple PyTorch example for a feedforward network.
import torch
import torch.nn as nn
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(10, 32)
self.fc2 = nn.Linear(32, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
return self.fc2(x)
model = Net()
optimizer = optim.Adam(model.parameters())
criterion = nn.MSELoss()
x = torch.randn(8, 10)
y = torch.randn(8, 1)
output = model(x)
loss = criterion(output, y)
loss.backward()
optimizer.step()
print("Loss:", loss.item())
هادا مثال بسيط ف PyTorch باش تدرب network صغيرة.
Exercises
- Write a Python script that prints the shape of a tensor.
- Create a NumPy matrix and compute gradients using PyTorch autograd.
- Build a simple neural network with two layers.
- Train a logistic regression model on any dataset.
- Implement a feedforward pass step by step.
- Plot a training curve using matplotlib.
- Train a CNN on any small image dataset.
- Fine tune a small transformer model.
- Save a PyTorch model and load it again.
- Deploy a small model with a local API.
Conclusion
Follow the steps and practice daily. Deep learning grows with repetition and clean project work.
تابع الخطوات و رجع للمفاهيم كل نهار باش تقوي مهاراتك.