Deep Learning | NetrAInsights

Level

Advanced

Duration

4 Weeks

Hands-On Labs

Format

Self-paced

What You'll Learn

Go deep into the architecture and training of modern neural networks. From backpropagation fundamentals to cutting-edge architectures like Vision Transformers and Diffusion Models, this course covers everything you need to build state-of-the-art deep learning systems.

Neural Network Fundamentals: Perceptrons, backprop, activations, optimizers
CNNs: ResNet, EfficientNet, image classification and object detection
RNNs & LSTMs: Sequence modeling, time series, text generation
Transformers: Self-attention, BERT, GPT, Vision Transformers
Generative Models: GANs, VAEs, Diffusion Models
Training at Scale: Mixed precision, distributed training, gradient checkpointing

Course Modules

🔢 Week 1: Neural Network Foundations▼

Perceptrons and multi-layer networks
Forward and backward propagation
Activation functions (ReLU, GELU, Swish)
Loss functions and optimizers (Adam, SGD)
Regularization (Dropout, BatchNorm, Weight Decay)
Lab 1: Implement neural network from scratch in NumPy
Lab 2: Build MLP with PyTorch
Lab 3: Hyperparameter tuning with Optuna
Lab 4: Debug vanishing gradients

📷 Week 2: Convolutional Neural Networks▼

Convolution, pooling, receptive fields
Classic architectures: VGG, ResNet, EfficientNet
Transfer learning and feature extraction
Object detection (YOLO, Faster R-CNN)
Semantic segmentation (U-Net, DeepLab)
Lab 5: Train image classifier on CIFAR-10
Lab 6: Fine-tune ResNet50 for custom dataset
Lab 7: Implement YOLO for object detection
Lab 8: Build medical image segmentation

🔄 Week 3: Sequence Models & Transformers▼

RNNs, LSTMs, GRUs for sequences
Attention mechanism from scratch
Transformer architecture (encoder-decoder)
BERT for classification, GPT for generation
Vision Transformers (ViT)
Lab 9: Build LSTM for text generation
Lab 10: Implement attention from scratch
Lab 11: Fine-tune BERT for sentiment analysis
Lab 12: Train ViT on image dataset

🎭 Week 4: Generative Models & Production▼

VAEs — latent space and reconstruction
GANs — generator, discriminator, training tricks
Diffusion models — DDPM, DDIM, score matching
Mixed precision and distributed training
Model optimization (quantization, pruning)
Lab 13: Train GAN on MNIST
Lab 14: Build image generation with Stable Diffusion
Lab 15: Distributed training with PyTorch DDP
Lab 16: Quantize model with GPTQ
Lab 17: Deploy model with TorchServe
Lab 18 (Capstone): Train custom deep learning model end-to-end

Prerequisites

Python programming (strong level)
Linear algebra (matrices, vectors, eigenvalues)
Calculus basics (derivatives, chain rule)
ML Fundamentals course or equivalent

Who Should Take This?

ML Engineers advancing beyond classical ML
Research Scientists building novel architectures
AI Engineers working on computer vision or NLP
Students preparing for top AI research roles

Tools & Tech Stack

Python, NumPy, PyTorch, torchvision
Hugging Face Transformers, Diffusers
Weights & Biases (experiment tracking)
CUDA, Google Colab / A100 GPUs
TorchServe, ONNX Runtime

Ready to Start?

Master the architectures that power modern AI — from image recognition to text generation to content creation.

📧 Enroll Now

🧠 Deep Learning