Become an AI Researcher — Build Your Own GPT, Transformers & Multimodal Models from Scratch in PyTorch (Urdu/Hindi)
Mean Squared Error (MSE)(L2 LOSS) Cost Function Explained & It's Derivative
Become an AI Researcher — Build Your Own GPT, Transformers & Multimodal Models from Scratch in PyTorch (Urdu/Hindi)
0% Completed
Programming & Data Science Fundamentals for AI
This section covers all the essential tools you need to start your journey in AI and Deep Learning. You’ll build a strong foundation in Python programming and core data science libraries including NumPy, Pandas, Matplotlib, and Seaborn.
You’ll learn how to write clean Python code, work with arrays and datasets, perform data analysis, and visualize results effectively—skills that are mandatory before moving into machine learning and deep learning with PyTorch.
Core Deep Learning Concepts – From Perceptron to Backpropagation
Dive into the most important foundations of deep learning. This section covers Perceptrons, Multi-Layer Perceptrons (MLPs), forward propagation, and backpropagation, giving you the practical understanding and skills to build neural networks from scratch. Each lecture is hands-on and explained step-by-step, so you can apply these concepts directly in PyTorch projects.
PyTorch: From Fundamentals to AI Research-Level Development
Master PyTorch from first principles, starting with tensors and autograd, and progressing to research-grade model development. Learn how to write clean, scalable, and experiment-ready code used in real AI labs and research teams. By the end, you’ll be able to read papers, implement architectures, and run serious AI experiments in PyTorch.
Apply your deep learning knowledge with a real-world Linear Regression project using PyTorch. This section walks you through data preparation, model building, training, and evaluation, giving you practical experience and confidence to implement AI models from scratch.
Learn the essential mathematics that powers artificial intelligence. This section covers linear algebra, calculus, probability, statistics, and discrete math, giving you the foundation to understand algorithms, machine learning, and AI models. Each concept is explained practically, so you can apply it to real-world AI problems with confidence.
All Activation Functions in Deep Learning – Explained with PyTorch
Learn and implement all major activation functions used in deep learning. This section covers: Linear, Threshold, Sigmoid, Tanh, Softmax, ReLU, LeakyReLU, Parametric ReLU (PReLU), ELU, SELU, Swish, Softplus, and Mish, with step-by-step explanations and practical PyTorch coding. By the end, you’ll know when and how to use each function in your neural networks to build efficient, high-performing AI models.
All Loss & Cost Functions in Deep Learning – Explained with PyTorch
Master all key loss and cost functions used in neural networks and AI. This section covers: Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Squared Error (RMSE), Root Mean Squared Log Error (RMSLE), Huber Loss (Smooth L1), Log-Cosh Loss, Binary Cross-Entropy (BCE), Categorical Cross-Entropy, and other important cost functions, with step-by-step PyTorch implementations. You’ll learn how each function works, when to use it, and how it impacts model performance.
All Optimizers in Deep Learning – Explained with PyTorch
Learn all major optimization algorithms that make neural networks train effectively. This section covers: Gradient Descent (Batch, Stochastic, Mini-Batch), Exponentially Weighted Moving Average (EWMA), SGD with Momentum, Nesterov Accelerated Gradient (NAG), AdaGrad, RMSProp, and Adam (Adaptive Moment Estimation) — all explained theoretically and implemented step-by-step in PyTorch. By the end, you’ll know how to select and apply the right optimizer to achieve faster convergence and better model performance.
Hands-On Project: Logistic Regression with PyTorch
Build practical skills with a Logistic Regression project in PyTorch. You’ll learn data preprocessing, model creation, training, and evaluation, giving you hands-on experience and a portfolio-ready AI project to showcase your machine learning expertise.
Hands-On Project: Classification with Neural Networks in PyTorch
Get practical experience building classification models using neural networks in PyTorch. This project walks you through data preparation, model building, training, and evaluation, giving beginners hands-on skills and a portfolio-ready project to showcase real AI expertise.
Learn how to optimize and stabilize your neural networks for better performance. This section covers: Vanishing & Exploding Gradients Overfitting & Underfitting Regularization Techniques: L1, L2, Elastic Net Dropout for Robust Models All Key Normalizations: Batch, Layer, Group, Instance, RMS, and input normalization All concepts are explained theoretically and implemented step-by-step in PyTorch, giving you hands-on experience to build efficient and high-performing AI models.
Master NLP techniques with hands-on PyTorch projects. This section covers: Sentiment Analysis using Word Embeddings and Neural Bag of Words Recurrent Neural Networks (LSTM) Sequence-to-Sequence (Seq2Seq) Models You’ll learn how to process text, build NLP models, and implement real-world applications, gaining practical experience to handle AI language tasks and build portfolio-ready projects.
Implementing Transformers from Scratch in PyTorch (Research-Driven Approach)
Implement the Transformer architecture line-by-line in PyTorch while systematically studying the original research paper. Translate mathematical formulations—self-attention, multi-head attention, positional encoding, and normalization—directly into working code. This section builds true architectural understanding, preparing you for LLM research, model scaling, and advanced AI engineering roles.
Computer Vision with PyTorch: From Fundamentals to Modern Architectures
Learn how machines see, understand, and reason about images using PyTorch. This section covers core computer vision concepts, feature learning, and modern deep learning approaches used in real-world systems. Build a strong foundation for image classification, detection, segmentation, and vision-based AI research.
Vision Transformer (ViT): From Research Paper to PyTorch Implementation
Implement the Vision Transformer from scratch in PyTorch by rigorously studying the original research paper. Translate patch embeddings, positional encoding, self-attention, and classification heads from theory into clean, modular code. Gain a deep research-level understanding of how Transformers replace CNNs in modern computer vision systems.
Generative Models – From Fundamentals to Advanced Architectures
Learn how AI systems create new data instead of just predicting it. This section builds strong intuition behind modern generative models, training strategies, and real-world use cases. A foundation that prepares you for advanced Generative AI, image synthesis, text generation, and future models.
BERT From Scratch in PyTorch – Research-Grade Implementation
In this section, you will implement BERT (Bidirectional Encoder Representations from Transformers) completely from scratch using PyTorch, without relying on high-level libraries. You’ll start by understanding the original BERT research paper and its core ideas, then translate each concept into clean, modular code.
You will build every major component step by step, including token embeddings, segment embeddings, positional encodings, multi-head self-attention, encoder stacks, and layer normalization. You will also implement Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) to understand how BERT is pre-trained in real research and industry settings.
By the end of this section, you won’t just use BERT—you’ll fully understand how it works internally, preparing you for LLM research, model fine-tuning, custom transformer architectures, and advanced NLP engineering roles.
What You’ll Build
A full BERT encoder architecture in PyTorch
Masked Language Modeling (MLM) training pipeline
Next Sentence Prediction (NSP) objective
A research-ready BERT implementation you can extend or fine-tune