Model Live Course
Duration
Modules 12 modules

Overview

Deep Learning with PyTorch Training comprehensively covers the full suite of tools offered by the PyTorch library for developing, training, and optimizing deep learning algorithms in a dynamic, flexible, and user-friendly environment. Starting from basic tensor operations, this training aims to build hands-on knowledge and skills across a wide spectrum—including building neural network architectures, model training, backpropagation, optimization, transfer learning, and model deployment.

What Is It?

PyTorch is an open-source, flexible deep learning library featuring a dynamic computation graph. The training provides detailed coverage of PyTorch's core building blocks—tensors, automatic differentiation (autograd), neural network layers, and modular design—while walking through practical model development workflows using CNNs, RNNs, and other foundational and modern architectures.

Who Is It For?

This training is suitable for:

  • Individuals interested in artificial intelligence and machine learning,

  • Data scientists and engineers,

  • Software developers looking to integrate deep learning algorithms into their projects,

  • Academics and researchers seeking to apply deep learning techniques in their work,

  • University students aiming to pursue a career in deep learning,

  • Professionals in healthcare, finance, automotive, and retail looking to learn how deep learning can be integrated into business workflows.

Why Deep Learning with PyTorch Training?

Deep learning is one of today's most exciting and in-demand technologies, making a major impact across both industry and academia. Taking deep learning training with Python and PyTorch provides the following advantages:

  • High Performance: Deep learning models can make human-level decisions on complex problems.

  • Broad Application Areas: Used across numerous domains including computer vision, natural language processing (NLP), robotics, and recommendation systems.

  • Powerful Libraries: Enables rapid prototyping and model development through powerful deep learning libraries and ecosystems such as PyTorch, TensorFlow, and Keras.

  • Big Data Analysis: Deep learning works effectively with large-scale and unstructured data.

  • Industry Demand: Companies need deep learning specialists across a wide range of applications, from autonomous systems to customer service.

Curriculum

Introduction to Deep Learning and PyTorch

Background of Deep Learning

  • Perceptron → Backpropagation → AlexNet

  • Transformer & Diffusion eras

Classical ML ↔ Deep Learning

  • Hand-crafted features ⇄ learned features

  • Data scale / compute requirements

  • Linear separability / deep representation power

PyTorch Ecosystem

  • TorchScript · torch-data · torch-metrics

  • torch.compile (PyTorch 2.x Dynamo)

Community & Release Lifecycle

  • RFC processes, LTS packages

  • pip vs conda distribution differences

Mathematical Foundations

  • Linear algebra : eigen, SVD

  • Calculus : chain rule, Jacobian / Hessian

  • Probability & information theory : KL divergence, cross-entropy

Tensor Practice

  • Broadcasting rules

  • Einstein summation → torch.einsum
PyTorch Core Internals

Tensor Lifecycle

  • pin_memory, shared memory

  • CPU ↔ GPU copying & pinning

Autograd Deep Dive

  • GradFn chain, torch.autograd.profiler

  • Gradient checkpointing, sub-linear memory compute

Dynamic Computation Graph

  • Eager Mode · TorchScript · TorchDynamo

Loss Functions & Metrics

  • Focal Loss · Dice Loss · InfoNCE

  • AUROC, Cohen’s κ, top-k accuracy

Optimizer Family

  • AdamW

  • RAdam

  • Lion

  • SAM

  • LAMB
Data Ecosystem & Preprocessing

torch-data & DataPipes

  • Streaming data, WebDataset, tar-shard structures

Image, Text & Audio Pipelines

  • Albumentations · Kornia augmentations

  • torchtext + SentencePiece tokenization

  • torchaudio : MelSpectrogram, noise injection

Big Data & Distributed Loading

  • NVIDIA DALI, Petastorm

  • Parquet ↔ Arrow, async prefetch
Training Loop & Core Networks

Anatomy of nn.Module

  • Parameter vs buffer registration

  • Modular layer design

Training Loop

  • Batch / epoch logic

  • Label smoothing

    • Mixup

    • CutMix

Toolkits

  • PyTorch Lightning, HF accelerate

  • Callbacks : early stop, gradient clip
Convolutional Networks & Computer Vision

Modern CNN Blocks

  • Inception · ResNet · DenseNet · EfficientNet-V2

Detection & Segmentation

  • YOLOv8 · DETR · Mask R-CNN · SAM

Self-Supervised Learning

  • SimCLR · BYOL · DINOv2 · MAE

Vision Transformer & Hybrids

  • ViT, Swin, conv-former designs

Hands-On Lab

  • Industrial defect detection

  • Supervised vs self-supervised comparison
Sequential Models & Attention

RNN Family

  • LSTM · GRU · Bi-RNN

  • Vanishing / exploding gradient solutions

Attention Mechanisms

  • Bahdanau · Luong

  • Additive vs dot-product · Multi-head

Seq2Seq & Time Series

  • Teacher forcing · Scheduled sampling

  • Temporal Fusion Transformer

Speech Recognition Pipeline

  • CTC Loss · Wav2Vec 2.0

Hands-On Lab

  • Turkish news summarization : BiLSTM-Attention vs mBART
Transformers & Large Language Models

Core Building Blocks

  • Flash Attention-2 · SwiGLU · Rotary PE

  • Long-context methods

Parameter-Efficient Tuning

  • LoRA · p-LoRA · Q-LoRA

  • Prefix / Prompt Tuning

Distributed Training

  • DDP · FSDP · ZeRO-3

  • DeepSpeed · Megatron-LM

LLM Alignment & Enhancement Strategies

  • RLHF · DPO · RAG-Fusion

  • Toolformer & Function-Calling

Multilingual / Culture-Specific LLMs

  • Llama-3-Instruct Turkish fine-tuning
Generative Models (GAN · VAE · Diffusion)

GAN Family

  • CGAN · WGAN-GP · StyleGAN-3

VAE Variants

  • β-VAE · VQ-VAE-2

Diffusion Models

  • DDPM · Latent Diffusion · Control-Net

Hands-On Workshop

  • Text-to-image generation with PyTorch + diffusers
Graph Neural Networks

Fundamentals & Ecosystem

  • Graph data structures, PyG · DGL

Core GNN Models

  • GCN · GraphSAGE · GAT · Graph Transformer

Hands-On Lab

  • Social network link prediction

  • Supply chain risk graph analysis
Hyperparameter Tuning & AutoML

Search Frameworks

  • Optuna · Ray Tune · Hyperband · PBT

Neural Architecture Search

  • DARTS · ProxylessNAS

Hands-On Workshop

  • Sweeps and WandB visualization
Model Compression, Acceleration & Deployment

Compression & Acceleration

  • Quantization : PTQ · QAT · GPTQ · AWQ

  • Pruning · Distillation

Compilers & Runtime

  • Torch-TensorRT · ONNX · TVM · vLLM

Edge & Mobile Deployment

  • TorchLite · CoreML · WebGPU

Microservice Architecture

  • FastAPI · BentoML · KServe
MLOps, Monitoring & Continuous Delivery

CI/CD Pipelines

  • GitHub Actions · GitLab CI · Jenkins

Monitoring & Drift

  • Evidently · WhyLabs · Arize

Observability & Profiling

  • OpenTelemetry · PyTorch Profiler

Security & Governance

  • Vault · SBOM · Cosign signing policies

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