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AI & Deep Learning

Deep Learning Specialization — From Neural Nets to Modern Architectures

The central deep-learning track spanning perceptrons, optimization, CNNs, RNNs, transformers, generative models, and graph neural networks.

📚 8 units 🪜 30 steps / lessons ⏱️ Self-paced
This course / diploma is coming soon
This course is coming soon — register your details and we'll notify you when it launches.
Expected launch: Q3 2026
Pricing on launch

Available as part of AI diploma bundles

الكورس ده قيد التحضير — سجل بياناتك وهنبلغك أول ما ينزل.

🚀 Enrollment opens soon 🔔 Sign up to be notified
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Projects0
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🎯 Why this diploma is worth it

Not a watch-and-forget course — a complete technical reference that lives with you

You get the videos, materials, quizzes, exams and projects — a solid, always-updated reference for every technology in the diploma. You won't need another source.

♾️

Lifetime reference + yearly updates

Come back anytime — at work, in interviews, in projects. Refreshed every year so it stays up to date.

📦

Everything in one place

Deep-dive videos + materials + quizzes + exams for every part — a complete curriculum, not just lectures.

⏱️

Learn at your own pace

Fully recorded. Finish on your schedule, complete a part each month, and progress step by step.

🛠️

Real projects on GitHub

0+ hands-on projects you submit and push to a GitHub repo — building a portfolio that convinces any employer.

🧑‍🏫

Manual review + industry instructors

Your projects are reviewed by hand, taught by instructors working in the industry — real feedback that levels you up.

🏆

Verified certificate + training letter

A QR-verifiable certificate + an Arabic/English training letter that convinces HR — ready for jobs and college.

3Learning outcomes
0Tools & tech
0Hands-on projects
★ 4.9Avg student rating
🚀 Start now — claim your spot in the diploma

What you will learn

  • ✅ Understand modern deep-learning architectures deeply
  • ✅ Train and debug neural systems with discipline
  • ✅ Build the theoretical bridge into NLP, CV, and GenAI

Curriculum & units

📚 8 units 🪜 30 steps ⏱️ Flexible
Unit 1: Neural Networks 3 topics · Flexible pace
  • Lesson 1.1: Perceptrons & MLPs — - Perceptron & Intro to NN.
  • Lesson 1.2: Activations & Losses — - Activation functions: ReLU, GELU, sigmoid, tanh
  • Lesson 1.3: Backpropagation — - Computational graphs.
Unit 2: Optimization & Initialization 2 topics · Flexible pace
  • Lesson 2.1: Optimization — - SGD as noisy gradient descent.
  • Lesson 2.2: Initialization — - Vanishing/exploding gradients
Unit 3: Training Discipline & Generalization 3 topics · Flexible pace
  • Lesson 3.1: Regularization — - Explicit Regularization: L1/L2 / Elasticnet (Weight Decay)
  • Lesson 3.2: Generalization Theory — - Classical View: Bias-Variance Tradeoff.
  • Lesson 3.3: Debugging Deep Learning — - The "Overfit a Single Batch" test
Unit 4: CNNs 7 topics · Flexible pace
  • Lesson 4.1: Convolution Mechanics — - Why not FC networks?
  • Lesson 4.2: Modern CNN blocks (Optional) — - Building blocks for Segmentation and Mobile apps.
  • Lesson 4.3: CNN Architectures I (The Classics) — - AlexNet: The proof of concept (ReLU + Dropout + GPU).
  • Lesson 4.4: CNN Architectures II (The Residual Era) — - ResNet: Skip Connection revolution.
  • Lesson 4.5: CNN Architectures III (Efficiency & Modernization) (Optional) — - MobileNet V2/V3: Inverted Residuals & Linear Bottlenecks.
  • Lesson 4.6: Transfer Learning & Finetuning — - Pre-training (ImageNet) vs. Fine-tuning (Custom Data).
  • Lesson 4.7: Attention in CNNs & Normalization — - Attention Mechanisms: Squeeze-and-Excitation (SE Blocks).
Unit 5: RNNs 7 topics · Flexible pace
  • Lesson 5.1: The Sequence Modeling Paradigm — - The Constraint of FFNs: Why standard networks fail on variable-length data.
  • Lesson 5.2: Training RNNs — - BPTT (Backpropagation Through Time): How gradients flow back through time steps.
  • Lesson 5.3: Gated Architectures (LSTM/GRU) — - The Cell State: How LSTMs preserve long-term dependencies.
  • Lesson 5.4: Advanced RNN Structures — - Bidirectional RNNs (Bi-LSTM): Looking at the future context (essential for tasks like Named Entity Recognition).
  • Lesson 5.5: The Encoder-Decoder (Seq2Seq) Architecture — - Encoder-Decoder Architecture: Compressing a sentence into a fixed vector z.
  • Lesson 5.6: Attention Mechanisms — - Recurrent Attention: Introduction to Bahdanau (Additive) and Luong (Multiplicative) attention.
  • Lesson 5.7: Practical Sequence Modelling — - Embeddings: Brief recap (Word2Vec/GloVe) vs. Learning embeddings from scratch.
Unit 6: Transformers 6 topics · Flexible pace
  • Lesson 6.1: The Transformer Paradigm Shift — - Recurrence vs. Parallelism: Why RNNs are slow (Sequential O(N)) and Transformers are fast (Parallel O(1)).
  • Lesson 6.2: Self-Attention — - The Query-Key-Value (QKV) Analogy: Database retrieval concepts mapped to vectors.
  • Lesson 6.3: Multi-Head Attention — - Concept: Why one attention head isn't enough.
  • Lesson 6.4: Tokenization & Positional Encodings — - The Order Problem: Why Transformers are permutation invariant.
  • Lesson 6.5: Transformer Architecture Details — - Position-wise Feed-Forward Networks.
  • Lesson 6.6: The Family Tree (BERT/GPT/T5) — - Encoder-Only (BERT-style): Bidirectional, for understanding/classification.
Unit 7: Generative Models 6 topics · Flexible pace
  • Lesson 7.1: Autoencoders — - Concept.
  • Lesson 7.2: Self-Supervised Learning (SSL) — - The Revolution: Why we stopped using Autoencoders for features and started using SSL.
  • Lesson 7.3: VAEs — - The Shift: Moving from deterministic to probabilistic embeddings.
  • Lesson 7.4: GANs — - KL Divergence, JS Divergence.
  • Lesson 7.5: Diffusion Models — - Replacing GANs for image generation.
  • Lesson 7.6: Evaluation Metrics (optional) — - How do we measure "creativity".
Unit 8: Graph NN (optional) 4 topics · Flexible pace
  • Lesson 8.1: From Grids to Graphs — - The Grid Limitation: CNNs assume fixed neighbors (top, bottom, left, right).
  • Lesson 8.2: Neural Message Passing — - The Analogy: GNN is just "Convolution with an arbitrary number of neighbors."
  • Lesson 8.3: Key Architectures — - GCN (Graph Convolutional Networks).
  • Lesson 8.4: Tasks & Applications — - Node Level: Classifying a user (bot vs. human).
Project 1 topics · Flexible pace
  • Deep Learning Project

Projects you will build

Tools & platforms

Target audience

  • ML engineers moving into DL
  • Researchers and advanced students
  • Specialists preparing for NLP/CV/GenAI tracks

Career paths

What you receive after finishing

Verification-ready certificates and HR-friendly training letters.

🏆

Verified Certificate

Official Learn in Depth completion certificate with QR verification.

Verifiable on the public verification page.

🇬🇧

English Training Letter

For international companies and overseas employment.

On official Learn in Depth letterhead, signed by the instructor.

🇪🇬

Arabic Training Letter

For local employers in MENA and university coordination.

Bilingual stamped letter ready for HR submission.

🏢

Company-Stamped Certificate

Company-stamped, for academic credit. Request it by contacting +20 155 876 5064 via WhatsApp or phone.

Issued upon request after successful completion.

Course FAQ

The current target is Q3 2026. Register via the form and we will send launch and pricing updates first.

المستهدف الحالي هو Q3 2026. سجّل بياناتك في الفورم وسنرسل لك أول تحديثات الإطلاق والأسعار.

These tracks are currently marked Coming Soon. You can browse the curriculum and leave your details to be notified when registration opens.

حاليًا المسارات في حالة Coming Soon. تقدر تتصفح المحتوى وتسجل اهتمامك لنبلغك عند فتح التسجيل.

Yes — every track is designed around a verified certificate and hands-on project review.

أيوة — كل مسار مصمم بشهادة موثقة ومراجعة عملية للمشاريع الرئيسية.

Yes. The positioning, support, and project design target engineers in the Egyptian and Gulf markets with a strong practical hiring focus.

أيوة، التسويق والدعم وبناء المشاريع معمولين خصيصًا لمهندسين السوق المصري والخليجي مع تركيز على التوظيف العملي.

Send a WhatsApp message to +201558765064 saying: "I need to reset my device ID" along with your email. We'll reset the old device and you can immediately access on the new one.

ابعت رسالة على واتساب +201558765064 وقول: "عايز أعمل reset لل ID بتاعي" واكتب الإيميل بتاعك. هنعمل Reset للجهاز القديم وتقدر تفتح على الجهاز الجديد فوراً.

Create your account, add the course to cart, and follow the payment steps.

سجل حسابك وأضف الكورس للسلة واتبع خطوات الدفع.

Yes — students get an automatic discount shown at checkout.

أيوه — الطلبة ليهم خصم خاص بيظهر أوتوماتيك.

All courses are recorded so you can learn at your own pace.

كل الكورسات مسجلة عشان تتعلم في أي وقت يناسبك.

Yes — all courses are free for people from Palestine.

أيوه — كل الكورسات مجانية لأهل فلسطين.

Bank transfer, Vodafone Cash, InstaPay.

تحويل بنكي، فودافون كاش، إنستاباي.

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