Time Series Forecasting with LSTM Networks in PyTorch
Learn to build deep learning models with LSTMs to analyze, predict, and forecast time-series data.
Date
Sat Apr 30
Time
08:00 PM
Duration
1 day
Tools & Technologies
Reserve Your Seat
₹499
one-time access4 modules
Lifetime access
Certificate of completion
Community access
Workshop Overview
Build Real Skills with Practical Learning
Learn to build deep learning models with LSTMs to analyze, predict, and forecast time-series data.
What You’ll Learn
How Neural Networks Think — Code a Neural Net from Scratch
Activation & Loss Functions - Code Activations
Limitations of Feedforward Models on Sequences
PyTorch for Deep Learning
Strong Understanding of RNNs & LSTM Models
Time Series forecasting Model using LSTM.
Meet Your Mentor
Expert Industry Mentor
Senior AI & ML Practitioner
Learn directly from professionals who have built real-world systems, shipped production-grade solutions, and trained teams across industries. This workshop is designed to give you practical clarity—not just theory.
Experience
10+ Years
Projects
50+ Delivered
Learners
10,000+
Workshop Agenda
Structured Learning, Real Implementation
A premium step-by-step workshop journey designed to take you from foundations to hands-on execution with practical clarity.
Module 1
Basics of Neural Networks
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
Module 2
Understanding RNNs
Why Feedforward Fails at Sequences
RNN Architecture & Workflow
Train Simple RNN
Limitations of RNNs
Module 3
Deep Dive into LSTM
Motivation: Long-Term Dependencies
LSTM Cell Architecture
Build LSTM Cell from Scratch (optional, for advanced)
Implement LSTM with Real Framework
Module 4
Building the Model
EDA
Time Series Forecasting Project
Preprocessing + Sequence Windowing
Build LSTM → Train → Evaluate
Plot forecasts vs actuals
Add dropout, stack LSTM layers, change lookback
Hands-On Projects
Learn by Building, Not Just Watching
Every premium workshop includes practical implementation so you leave with execution confidence, not just theoretical understanding.
Real-World Capstone Project
Build a production-style project that applies everything learned during the workshop with practical implementation.
Industry Workflow Simulation
Work through realistic problem-solving scenarios similar to professional team environments and delivery pipelines.
Portfolio-Ready Final Output
Create something you can confidently showcase in interviews, resumes, and professional profiles.
Learner Testimonials
Trusted by Serious Learners
Real outcomes from professionals and students who joined our premium workshops and turned learning into practical execution.
This workshop completely changed how I approach real-world ML problems. The practical depth and mentor guidance were exceptional.
Arjun R.
Data Analyst
Unlike regular online courses, this felt like true industry training. I left with confidence and a strong portfolio project.
Sneha K.
Software Engineer
The hands-on structure, live implementation, and clarity of teaching made this one of the best workshops I’ve attended.
Rahul P.
Graduate Student
What Sets Our Learning Experience Apart
Structured paths, real-world execution, and a system designed to make you consistent.
01 — STRUCTURED LEARNING
From confusion to clarity with guided, live sessions
We don’t just give content. We guide you step-by-step with live sessions, ensuring concepts are deeply understood and applied.
02 — COMMUNITY
Learn with a cohort that keeps you accountable
Stay consistent with peer-driven momentum and shared progress.
03 — PRACTICAL EXECUTION
Turn theory into real-world projects
Apply everything through structured assignments and real use cases.
04 — MENTORSHIP
Learn directly from industry experts
Get guidance from practitioners who have built real systems.
05 — CONSISTENCY SYSTEM
A system designed to keep you consistent
Structured timelines and accountability loops ensure you don’t drop off.