DEEP LEARNING – Bythar

About Course

Deep learning powers everything from image recognition to large language models, and this course takes you from the fundamentals all the way to deploying real models in production. Built entirely in PyTorch, you will work hands-on through neural networks, computer vision, natural language processing, time series forecasting, and transfer learning, backed by the evaluation, debugging, and MLOps skills that separate a working model from a production-ready one.

Every topic is taught the way it is actually used in industry: you will train models on real datasets, fine-tune pretrained architectures, and package a model for deployment, not just watch theory slides. By the end, you will have the practical deep learning foundation needed to build, evaluate, and ship your own models.

Build and train neural networks from scratch using PyTorch
Design and train convolutional neural networks for computer vision tasks
Process and model text using RNNs, LSTMs, and Transformer architectures
Forecast sequential data using LSTM-based time series models
Fine-tune pretrained CNNs and language models for your own tasks
Evaluate, regularize, and debug deep learning models like a practitioner
Package, track, and deploy deep learning models using real MLOps tools
Develop a practical, project-based deep learning workflow you can reuse on any dataset

Course Content

Introduction to Deep Learning with PyTorch

What Is Deep Learning? From Machine Learning to Neural Networks
Setting Up Your PyTorch Environment
PyTorch Fundamentals: Tensors, Autograd, a Simple Workflow, and GPU/Device Management
Introduction to Deep Learning with PyTorch Quiz

Introduction to Building Neural Networks

Computer Vision

Natural Language Processing

Time Series

Transfer Learning

Model Evaluation, Regularization and Debugging

MLOps for Deep Learning

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About Course

What Will You Learn?

Course Content

Introduction to Deep Learning with PyTorch

What Is Deep Learning? From Machine Learning to Neural Networks

Setting Up Your PyTorch Environment

PyTorch Fundamentals: Tensors, Autograd, a Simple Workflow, and GPU/Device Management

Introduction to Deep Learning with PyTorch Quiz

Introduction to Building Neural Networks

Neural Network Anatomy, Activation Functions, and Building Your First Network with torch.nn

Training Neural Networks: Loss Functions, Optimizers, and the Training Loop

Multilayer Perceptrons: Building and Training an MLP for Tabular Data Classification

Introduction to Building Neural Networks Quiz

Computer Vision

Introduction to Convolutional Neural Networks: Convolutions, Pooling, and Feature Maps

Building and Training a CNN with PyTorch (Prebuilt Dataset)

Training a CNN on a Custom Dataset: Preprocessing and Augmentation with torchvision

Computer Vision Quiz

Natural Language Processing

Text Preprocessing, Tokenization, and Word Embeddings (Word2Vec and GloVe)

Sequence Models: RNNs and LSTMs

Transformers and Attention: How the Architecture Works

Natural Language Processing Quiz

Time Series

Time Series Fundamentals: Trend, Seasonality, and Preparing Sequential Data

Forecasting with LSTM: Building a Sequence Model for Time Series

Time Series Quiz

Transfer Learning

What Is Transfer Learning? Feature Extraction vs Fine-Tuning, and a Tour of Common Pretrained Architectures (LeNet, VGG, ResNet)

Fine-Tuning Pretrained CNNs for Image Classification

Fine-Tuning Pretrained Language Models for NLP Tasks

Transfer Learning Quiz

Model Evaluation, Regularization and Debugging

Evaluation Metrics, Cross-Validation, and Train/Val/Test Splits

Regularization Techniques: Dropout, Weight Decay, Batch Normalization, and Diagnosing Overfitting/Underfitting

Debugging Neural Networks and Hyperparameter Tuning Strategies

Model Evaluation, Regularization and Debugging Quiz

MLOps for Deep Learning

Introduction to MLOps, and Saving/Loading/Versioning PyTorch Models

Model Packaging and Serving with TorchServe or FastAPI

Experiment Tracking (MLflow / Weights and Biases) and Monitoring Models in Production

Hands-On: Deploying a Deep Learning Model as an API

MLOps for Deep Learning Quiz

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