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AI & Deep Learning

Deep Learning Specialization โ€” From Neural Nets to Modern Architectures

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

๐Ÿ“š 8 units ๐Ÿชœ 30 steps / lessons โฑ๏ธ Self-paced
This course / diploma is coming soon
This course is coming soon โ€” 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

ุงู„ูƒูˆุฑุณ ุฏู‡ ู‚ูŠุฏ ุงู„ุชุญุถูŠุฑ โ€” ุณุฌู„ ุจูŠุงู†ุงุชูƒ ูˆู‡ู†ุจู„ุบูƒ ุฃูˆู„ ู…ุง ูŠู†ุฒู„.

๐Ÿš€ Enrollment opens soon ๐Ÿ”” Sign up to be notified
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What you will learn

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

Curriculum & units

๐Ÿ“š 8 units ๐Ÿชœ 30 steps โฑ๏ธ Flexible
Unit 1: Neural Networks 3 topics ยท Flexible pace
  • Lesson 1.1: Perceptrons & MLPs โ€” - Perceptron & Intro to NN.
  • Lesson 1.2: Activations & Losses โ€” - Activation functions: ReLU, GELU, sigmoid, tanh
  • Lesson 1.3: Backpropagation โ€” - Computational graphs.
Unit 2: Optimization & Initialization 2 topics ยท Flexible pace
  • Lesson 2.1: Optimization โ€” - SGD as noisy gradient descent.
  • Lesson 2.2: Initialization โ€” - Vanishing/exploding gradients
Unit 3: Training Discipline & Generalization 3 topics ยท Flexible pace
  • Lesson 3.1: Regularization โ€” - Explicit Regularization: L1/L2 / Elasticnet (Weight Decay)
  • Lesson 3.2: Generalization Theory โ€” - Classical View: Bias-Variance Tradeoff.
  • Lesson 3.3: Debugging Deep Learning โ€” - The "Overfit a Single Batch" test
Unit 4: CNNs 7 topics ยท Flexible pace
  • Lesson 4.1: Convolution Mechanics โ€” - Why not FC networks?
  • Lesson 4.2: Modern CNN blocks (Optional) โ€” - Building blocks for Segmentation and Mobile apps.
  • Lesson 4.3: CNN Architectures I (The Classics) โ€” - AlexNet: The proof of concept (ReLU + Dropout + GPU).
  • Lesson 4.4: CNN Architectures II (The Residual Era) โ€” - ResNet: Skip Connection revolution.
  • Lesson 4.5: CNN Architectures III (Efficiency & Modernization) (Optional) โ€” - MobileNet V2/V3: Inverted Residuals & Linear Bottlenecks.
  • Lesson 4.6: Transfer Learning & Finetuning โ€” - Pre-training (ImageNet) vs. Fine-tuning (Custom Data).
  • Lesson 4.7: Attention in CNNs & Normalization โ€” - Attention Mechanisms: Squeeze-and-Excitation (SE Blocks).
Unit 5: RNNs 7 topics ยท Flexible pace
  • Lesson 5.1: The Sequence Modeling Paradigm โ€” - The Constraint of FFNs: Why standard networks fail on variable-length data.
  • Lesson 5.2: Training RNNs โ€” - BPTT (Backpropagation Through Time): How gradients flow back through time steps.
  • Lesson 5.3: Gated Architectures (LSTM/GRU) โ€” - The Cell State: How LSTMs preserve long-term dependencies.
  • Lesson 5.4: Advanced RNN Structures โ€” - Bidirectional RNNs (Bi-LSTM): Looking at the future context (essential for tasks like Named Entity Recognition).
  • Lesson 5.5: The Encoder-Decoder (Seq2Seq) Architecture โ€” - Encoder-Decoder Architecture: Compressing a sentence into a fixed vector z.
  • Lesson 5.6: Attention Mechanisms โ€” - Recurrent Attention: Introduction to Bahdanau (Additive) and Luong (Multiplicative) attention.
  • Lesson 5.7: Practical Sequence Modelling โ€” - Embeddings: Brief recap (Word2Vec/GloVe) vs. Learning embeddings from scratch.
Unit 6: Transformers 6 topics ยท Flexible pace
  • Lesson 6.1: The Transformer Paradigm Shift โ€” - Recurrence vs. Parallelism: Why RNNs are slow (Sequential O(N)) and Transformers are fast (Parallel O(1)).
  • Lesson 6.2: Self-Attention โ€” - The Query-Key-Value (QKV) Analogy: Database retrieval concepts mapped to vectors.
  • Lesson 6.3: Multi-Head Attention โ€” - Concept: Why one attention head isn't enough.
  • Lesson 6.4: Tokenization & Positional Encodings โ€” - The Order Problem: Why Transformers are permutation invariant.
  • Lesson 6.5: Transformer Architecture Details โ€” - Position-wise Feed-Forward Networks.
  • Lesson 6.6: The Family Tree (BERT/GPT/T5) โ€” - Encoder-Only (BERT-style): Bidirectional, for understanding/classification.
Unit 7: Generative Models 6 topics ยท Flexible pace
  • Lesson 7.1: Autoencoders โ€” - Concept.
  • Lesson 7.2: Self-Supervised Learning (SSL) โ€” - The Revolution: Why we stopped using Autoencoders for features and started using SSL.
  • Lesson 7.3: VAEs โ€” - The Shift: Moving from deterministic to probabilistic embeddings.
  • Lesson 7.4: GANs โ€” - KL Divergence, JS Divergence.
  • Lesson 7.5: Diffusion Models โ€” - Replacing GANs for image generation.
  • Lesson 7.6: Evaluation Metrics (optional) โ€” - How do we measure "creativity".
Unit 8: Graph NN (optional) 4 topics ยท Flexible pace
  • Lesson 8.1: From Grids to Graphs โ€” - The Grid Limitation: CNNs assume fixed neighbors (top, bottom, left, right).
  • Lesson 8.2: Neural Message Passing โ€” - The Analogy: GNN is just "Convolution with an arbitrary number of neighbors."
  • Lesson 8.3: Key Architectures โ€” - GCN (Graph Convolutional Networks).
  • Lesson 8.4: Tasks & Applications โ€” - Node Level: Classifying a user (bot vs. human).
Project 1 topics ยท 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.

๐Ÿ†

Verified Certificate

Official Learn in Depth completion certificate with QR verification.

Verifiable on the public verification page.

๐Ÿ‡ฌ๐Ÿ‡ง

English Training Letter

For international companies and overseas employment.

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

๐Ÿ‡ช๐Ÿ‡ฌ

Arabic Training Letter

For local employers in MENA and university coordination.

Bilingual stamped letter ready for HR submission.

๐Ÿข

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.

ุงู„ู…ุณุชู‡ุฏู ุงู„ุญุงู„ูŠ ู‡ูˆ Q3 2026. ุณุฌู‘ู„ ุจูŠุงู†ุงุชูƒ ููŠ ุงู„ููˆุฑู… ูˆุณู†ุฑุณู„ ู„ูƒ ุฃูˆู„ ุชุญุฏูŠุซุงุช ุงู„ุฅุทู„ุงู‚ ูˆุงู„ุฃุณุนุงุฑ.

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

ุญุงู„ูŠู‹ุง ุงู„ู…ุณุงุฑุงุช ููŠ ุญุงู„ุฉ Coming Soon. ุชู‚ุฏุฑ ุชุชุตูุญ ุงู„ู…ุญุชูˆู‰ ูˆุชุณุฌู„ ุงู‡ุชู…ุงู…ูƒ ู„ู†ุจู„ุบูƒ ุนู†ุฏ ูุชุญ ุงู„ุชุณุฌูŠู„.

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

ุฃูŠูˆุฉ โ€” ูƒู„ ู…ุณุงุฑ ู…ุตู…ู… ุจุดู‡ุงุฏุฉ ู…ูˆุซู‚ุฉ ูˆู…ุฑุงุฌุนุฉ ุนู…ู„ูŠุฉ ู„ู„ู…ุดุงุฑูŠุน ุงู„ุฑุฆูŠุณูŠุฉ.

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

ุฃูŠูˆุฉุŒ ุงู„ุชุณูˆูŠู‚ ูˆุงู„ุฏุนู… ูˆุจู†ุงุก ุงู„ู…ุดุงุฑูŠุน ู…ุนู…ูˆู„ูŠู† ุฎุตูŠุตู‹ุง ู„ู…ู‡ู†ุฏุณูŠู† ุงู„ุณูˆู‚ ุงู„ู…ุตุฑูŠ ูˆุงู„ุฎู„ูŠุฌูŠ ู…ุน ุชุฑูƒูŠุฒ ุนู„ู‰ ุงู„ุชูˆุธูŠู ุงู„ุนู…ู„ูŠ.

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

ุณุฌู„ ุญุณุงุจูƒ ูˆุฃุถู ุงู„ูƒูˆุฑุณ ู„ู„ุณู„ุฉ ูˆุงุชุจุน ุฎุทูˆุงุช ุงู„ุฏูุน.

Yes โ€” students get an automatic discount shown at checkout.

ุฃูŠูˆู‡ โ€” ุงู„ุทู„ุจุฉ ู„ูŠู‡ู… ุฎุตู… ุฎุงุต ุจูŠุธู‡ุฑ ุฃูˆุชูˆู…ุงุชูŠูƒ.

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

ูƒู„ ุงู„ูƒูˆุฑุณุงุช ู…ุณุฌู„ุฉ ุนุดุงู† ุชุชุนู„ู… ููŠ ุฃูŠ ูˆู‚ุช ูŠู†ุงุณุจูƒ.

Yes โ€” all courses are free for people from Palestine.

ุฃูŠูˆู‡ โ€” ูƒู„ ุงู„ูƒูˆุฑุณุงุช ู…ุฌุงู†ูŠุฉ ู„ุฃู‡ู„ ูู„ุณุทูŠู†.

Bank transfer, Vodafone Cash, InstaPay.

ุชุญูˆูŠู„ ุจู†ูƒูŠุŒ ููˆุฏุงููˆู† ูƒุงุดุŒ ุฅู†ุณุชุงุจุงูŠ.

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