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Deep Learning

Neural network architectures, backpropagation, distributed training, GPU optimization, and advanced deep learning concepts.

44 concepts2 questions8 projects

Overview

Deep Learning is the subfield of machine learning focused on neural networks with multiple layers. These models learn hierarchical representations of data, enabling breakthroughs in vision, language, speech, and generative AI.

Core concepts include neural network fundamentals (activation functions, loss functions, backpropagation, gradient descent), architectures (feedforward, convolutional, recurrent, transformer), and training techniques (learning rate scheduling, batch normalization, dropout, weight initialization).

Advanced topics include distributed training (data parallelism, model parallelism, pipeline parallelism, DeepSpeed, FSDP), GPU optimization (mixed precision training, gradient checkpointing, memory management), and model compression (quantization, pruning, knowledge distillation). Understanding these concepts is essential for training and deploying large-scale models.

Deep Learning

Overview

ML Concepts

Explain backpropagation. How does it work?

How do you choose the number of epochs? Explain early stopping and gradient descent optimizers.

Deep-Dive Concepts (from Projects)

Transfer Learning with BERT

Model Quantization for Production

Evaluation Metrics Beyond Accuracy

FastAPI for ML Model Serving

Transfer Learning in Computer Vision

Model Optimization: ONNX and Quantization

Grad-CAM: Model Interpretability

FastAPI for ML Model Serving

Matrix Factorization Deep Dive

Two-Stage Recommendation Architecture

Recommendation Evaluation Metrics

Cold-Start Problem Solutions

Diversity in Recommendations

Implicit Feedback in Recommendations

QLoRA: Quantized Low-Rank Adaptation

vLLM Inference Optimization

TGI: Text Generation Inference

FSDP: Fully Sharded Data Parallel

DeepSpeed ZeRO: Zero Redundancy Optimizer

Multi-Node Training Networking

PPO: Proximal Policy Optimization

Reward Shaping for Trading

Parallelism Taxonomy

Chinchilla Scaling Laws

Contrastive Learning (MoCo)

Self-Distillation (DINO)

Grad-CAM for Multi-Label Classification

Domain Shift in Medical Imaging

Test-Time Adaptation (TENT)

Confidence Calibration

LoRA vs Full Fine-Tuning

Feature Store Architecture

Spot Training Economics

Multi-Model Endpoints

Model Monitor Metrics

SageMaker Pipelines vs Step Functions

Serverless Inference Trade-offs

GPU Instance Selection

Clarify Bias Metrics

SageMaker vs Self-Hosted MLOps

Distributed Training Strategies

MLOps Platform Comparison: SageMaker vs Vertex AI vs Kubeflow vs Databricks