Time Series Forecasting with LSTM Networks in PyTorch

Overview

What you'll learn

Key concepts and practical areas covered in this workshop.

• 01How Neural Networks Think — Code a Neural Net from Scratch
• 02Activation & Loss Functions - Code Activations
• 03Limitations of Feedforward Models on Sequences
• 04PyTorch for Deep Learning
• 05Strong Understanding of RNNs & LSTM Models
• 06Time Series forecasting Model using LSTM.

Curriculum

Workshop curriculum

Review the topics covered in each module before sending an enquiry.

1. 01

   Basics of Neural Networks

   2 weeks

   • What is neural network? - Architecture,Propogations
   • Activation Functions - ReLU, Sigmoid, Tanh
   • Loss Function MSE, MAE
   • Training simple Feedforward Neural Network
   • FFN for Sequence predictions
   • Practical Understanding
2. 02

   Understanding RNNs

   1 week

   • Why Feedforward Fails at Sequences
   • RNN Architecture & Workflow
   • Train Simple RNN
   • Limitations of RNNs
3. 03

   Deep Dive into LSTM

   3 weeks

   • Motivation: Long-Term Dependencies
   • LSTM Cell Architecture
   • Build LSTM Cell from Scratch (optional, for advanced)
   • Implement LSTM with Real Framework
4. 04

   Building the Model

   2 weeks

   • EDA
   • Time Series Forecasting Project
   • Preprocessing + Sequence Windowing
   • Build LSTM → Train → Evaluate
   • Plot forecasts vs actuals
   • Add dropout, stack LSTM layers, change lookback

Before you begin

What you'll need

• Laptop with internet connection
• Basic logical thinking
• No prior AI experience needed