About this programme
Programme overview
The Deep Learning is a 24-week, self-paced career-focused program designed to take learners from deep learning fundamentals to advanced architectures and production-ready model deployment. The curriculum covers mathematics for deep learning, PyTorch, neural networks, CNNs, RNNs, LSTMs, GRUs, Transformers, attention mechanisms, GANs, diffusion models, and model deployment.
Learners will gain hands-on experience through practical projects involving image classification, sequence forecasting, transformer fine-tuning, generative models, object detection, and model serving. The program also introduces industry tools such as PyTorch, Hugging Face Transformers and Diffusers, Weights & Biases, MLflow, ONNX Runtime, TensorRT, Optuna, and Ray Tune.
The final phases focus on transforming technical skills into career opportunities through capstone projects, portfolio development, ATS-optimized resume building, LinkedIn optimization, interview preparation, mock interviews, application tracking, networking, referrals, outreach, and offer negotiation. Learners complete practical deliverables throughout the program, including projects, a professional portfolio, resume, interview preparation, and an active job-search tracker.
By the end of the program, learners will have a strong foundation in deep learning, experience building and deploying modern deep learning solutions, multiple portfolio-ready projects, and a structured career-readiness pathway aimed at Deep Learning Engineer opportunities.
Curriculum
What you'll learn
Linear Algebra for Deep Learning
Lesson 1: Linear Algebra for Deep LearningLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 0 — Math Refresher & PyTorch BasicsWeek: 1Category: CoreLesson...
Calculus & Backpropagation Intuition
Lesson 2: Calculus & Backpropagation IntuitionLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 0 — Math Refresher & PyTorch BasicsWeek: 1Category: Co...
Tensors & Autograd
Lesson 3: Tensors & AutogradLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 0 — Math Refresher & PyTorch BasicsWeek: 1Category: CoreLesson Descripti...
PyTorch nn.Module Basics
Lesson 4: PyTorch nn.Module BasicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 0 — Math Refresher & PyTorch BasicsWeek: 2Category: CoreLesson Descrip...
GPU/CPU Device Management
Lesson 5: GPU/CPU Device ManagementLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 0 — Math Refresher & PyTorch BasicsWeek: 2Category: CoreLesson Descri...
Vectorization with NumPy
Lesson 6: Vectorization with NumPyLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 0 — Math Refresher & PyTorch BasicsWeek: 2Category: CoreLesson Descrip...
From-Scratch Backpropagation Notebook
Perceptron & MLP Architecture
Lesson 1: Perceptron & MLP ArchitectureLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 1 — Neural Network FundamentalsWeek: 3Category: CoreLesson Descri...
Activation Functions
Lesson 2: Activation FunctionsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 1 — Neural Network FundamentalsWeek: 3Category: CoreLesson DescriptionThis les...
Loss Functions
Lesson 3: Loss FunctionsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 1 — Neural Network FundamentalsWeek: 3Category: CoreLesson DescriptionThis lesson in...
Optimizers — SGD, Adam & RMSProp
Lesson 4: Optimizers — SGD, Adam & RMSPropLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 1 — Neural Network FundamentalsWeek: 4Category: CoreLesson Des...
Weight Initialization
Lesson 5: Weight InitializationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 1 — Neural Network FundamentalsWeek: 4Category: CoreLesson DescriptionThis le...
Forward & Backward Pass Tracing
Lesson 6: Forward & Backward Pass TracingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 1 — Neural Network FundamentalsWeek: 4Category: CoreLesson Desc...
MLP Classification Model from Scratch
Convolution & Pooling Operations
Lesson 1: Convolution & Pooling OperationsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 2 — CNNs: Image Classification & Transfer LearningWeek: 5C...
ResNet & EfficientNet
Lesson 2: ResNet & EfficientNetLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 2 — CNNs: Image Classification & Transfer LearningWeek: 5Category: Co...
Data Augmentation
Lesson 3: Data AugmentationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 2 — CNNs: Image Classification & Transfer LearningWeek: 5Category: CoreLesson...
Transfer Learning & Fine-Tuning
Lesson 4: Transfer Learning & Fine-TuningLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 2 — CNNs: Image Classification & Transfer LearningWeek: 6Ca...
Feature Map Visualization
Lesson 5: Feature Map VisualizationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 2 — CNNs: Image Classification & Transfer LearningWeek: 6Category: Co...
Fine-Tuned CNN for Image Classification
Vanilla RNN & Vanishing Gradients
Lesson 1: Vanilla RNN & Vanishing GradientsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 3 — RNNs, LSTMs & GRUs for Sequence DataWeek: 7Category:...
LSTM & GRU Gating
Lesson 2: LSTM & GRU GatingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 3 — RNNs, LSTMs & GRUs for Sequence DataWeek: 7Category: CoreLesson Descr...
Sequence-to-Sequence Framing
Lesson 3: Sequence-to-Sequence FramingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 3 — RNNs, LSTMs & GRUs for Sequence DataWeek: 7Category: CoreLesso...
Padding, Masking & Batching
Lesson 4: Padding, Masking & BatchingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 3 — RNNs, LSTMs & GRUs for Sequence DataWeek: 7Category: CoreLe...
LSTM-Based Sequence Model
Dropout & Batch/Layer Normalization
Lesson 1: Dropout & Batch/Layer NormalizationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 4 — Regularization, Tuning & Experiment TrackingWeek: 8...
L1/L2 Regularization & Early Stopping
Lesson 2: L1/L2 Regularization & Early StoppingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 4 — Regularization, Tuning & Experiment TrackingWeek:...
Learning-Rate Schedules
Lesson 3: Learning-Rate SchedulesLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 4 — Regularization, Tuning & Experiment TrackingWeek: 8Category: CoreLe...
Hyperparameter Search with Optuna & Ray Tune
Lesson 4: Hyperparameter Search with Optuna & Ray TuneLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 4 — Regularization, Tuning & Experiment Tracki...
Weights & Biases Logging
Lesson 5: Weights & Biases LoggingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 4 — Regularization, Tuning & Experiment TrackingWeek: 9Category: C...
Reproducibility Practices
Lesson 6: Reproducibility PracticesLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 4 — Regularization, Tuning & Experiment TrackingWeek: 9Category: Core...
Experiment Tracking & Hyperparameter Tuning
Self & Multi-Head Attention
Lesson 1: Self & Multi-Head AttentionLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 5 — Transformers & Attention MechanismsWeek: 10Category: AILess...
Positional Encoding
Lesson 2: Positional EncodingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 5 — Transformers & Attention MechanismsWeek: 10Category: AILesson Descripti...
Encoder/Decoder Architectures
Lesson 3: Encoder/Decoder ArchitecturesLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 5 — Transformers & Attention MechanismsWeek: 11Category: AILesson...
Vision Transformers (ViT)
Lesson 4: Vision Transformers (ViT)Learning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 5 — Transformers & Attention MechanismsWeek: 11Category: AILesson Des...
Fine-Tuning Pretrained Transformers
Lesson 5: Fine-Tuning Pretrained TransformersLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 5 — Transformers & Attention MechanismsWeek: 12Category: AI...
Tokenization Strategies
Lesson 6: Tokenization StrategiesLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 5 — Transformers & Attention MechanismsWeek: 12Category: AILesson Descr...
Fine-Tuned Transformer Model
GAN Generator & Discriminator Dynamics
Lesson 1: GAN Generator & Discriminator DynamicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 6 — Generative Models: GANs & Diffusion BasicsWeek:...
Mode Collapse & Training Stability
Lesson 2: Mode Collapse & Training StabilityLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 6 — Generative Models: GANs & Diffusion BasicsWeek: 13Ca...
Diffusion Fundamentals
Lesson 3: Diffusion FundamentalsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 6 — Generative Models: GANs & Diffusion BasicsWeek: 13Category: AILesson...
Conditional Generation
Lesson 4: Conditional GenerationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 6 — Generative Models: GANs & Diffusion BasicsWeek: 14Category: AILesson...
Evaluation Metrics (FID & IS)
Lesson 5: Evaluation Metrics (FID & IS)Learning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 6 — Generative Models: GANs & Diffusion BasicsWeek: 14Categor...
Untitled
Model Export to ONNX
Lesson 1: Model Export to ONNXLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 7 — Deploying & Scaling Deep Learning ModelsWeek: 15Category: AILesson Des...
TorchServe & TensorFlow Serving
Lesson 2: TorchServe & TensorFlow ServingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 7 — Deploying & Scaling Deep Learning ModelsWeek: 15Categor...
Quantization & Pruning
Lesson 3: Quantization & PruningLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 7 — Deploying & Scaling Deep Learning ModelsWeek: 15Category: AILess...
Distributed & Multi-GPU Training Basics
Lesson 4: Distributed & Multi-GPU Training BasicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 7 — Deploying & Scaling Deep Learning ModelsWeek: 1...
Batching & Latency Optimization
Lesson 5: Batching & Latency OptimizationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 7 — Deploying & Scaling Deep Learning ModelsWeek: 16Categor...
Model Versioning
Lesson 6: Model VersioningLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 7 — Deploying & Scaling Deep Learning ModelsWeek: 16Category: AILesson Descrip...
Quantized Model Deployment & Performance Benchmarking
End-to-End Deep Learning Problem Framing
Lesson 1: End-to-End Deep Learning Problem FramingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 8 — Capstone ProjectsWeek: 17Category: CareerLesson Descri...
Combining Architecture & Deployment
Lesson 2: Combining Architecture & DeploymentLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 8 — Capstone ProjectsWeek: 17Category: CareerLesson Descrip...
Documentation & Reproducibility
Lesson 3: Documentation & ReproducibilityLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 8 — Capstone ProjectsWeek: 18Category: CareerLesson Description...
Deep Learning Capstone Projects
Site Structure
Lesson 1: Site StructureLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 9 — Portfolio CreationWeek: 19Category: CareerLesson DescriptionThis lesson introduc...
Case-Study Format
Lesson 2: Case-Study FormatLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 9 — Portfolio CreationWeek: 19Category: CareerLesson DescriptionThis lesson intro...
GitHub Hygiene
Lesson 3: GitHub HygieneLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 9 — Portfolio CreationWeek: 19Category: CareerLesson DescriptionThis lesson introduc...
Deep Learning Portfolio Development
ATS Formatting
Lesson 1: ATS FormattingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 10 — Resume BuildingWeek: 20Category: CareerLesson DescriptionThis lesson introduces...
Quantified Resume Bullets
Lesson 2: Quantified Resume BulletsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 10 — Resume BuildingWeek: 20Category: CareerLesson DescriptionThis lesson...
LinkedIn Optimization
Lesson 3: LinkedIn OptimizationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 10 — Resume BuildingWeek: 20Category: CareerLesson DescriptionThis lesson int...
ATS-Optimized Resume for Deep Learning Engineer Roles
Deep Learning Theory & Mathematics
Lesson 1: Deep Learning Theory & MathematicsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 11 — Interview PreparationWeek: 21Category: CareerLesson Des...
PyTorch & Coding Exercises
Lesson 2: PyTorch & Coding ExercisesLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 11 — Interview PreparationWeek: 21Category: CareerLesson Description...
System Design for Deep Learning
Lesson 3: System Design for Deep LearningLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 11 — Interview PreparationWeek: 21Category: CareerLesson Descriptio...
Behavioral Interviews & STAR Method
Lesson 4: Behavioral Interviews & STAR MethodLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 11 — Interview PreparationWeek: 21Category: CareerLesson De...
Mock Interviews
Lesson 5: Mock InterviewsLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 11 — Interview PreparationWeek: 21Category: CareerLesson DescriptionThis lesson pro...
Deep Learning Interview Preparation
Offer Negotiation
Lesson 4: Offer NegotiationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 12 — Job Search & OutreachWeek: 24Category: CareerLesson DescriptionThis less...
Referrals & Cold Outreach
Lesson 2: Referrals & Cold OutreachLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 12 — Job Search & OutreachWeek: 22–23Category: CareerLesson Descr...
Application Tracking
Lesson 3: Application TrackingLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 12 — Job Search & OutreachWeek: 23Category: CareerLesson DescriptionThis l...
Offer Negotiation
Lesson 4: Offer NegotiationLearning Format: Self-Paced CurriculumRecommended Learning Time: 12–15 Hours per WeekPhase: 12 — Job Search & OutreachWeek: 24Category: CareerLesson DescriptionThis less...
Job Search & Application Tracking
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