Become an AI Researcher — Build Your Own GPT, Transformers & Multimodal Models from Scratch in PyTorch (Urdu/Hindi)
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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.
intro
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PYTHON Full Course for Beginners
Video - 16:43:34 mins
Python Numpy Full Tutorial For Beginners
Video - 4:33:00 mins
PANDAS Full Course with PRACTICAL
Video - 1:42:05 mins
Matplotlib Full Tutorial
Video - 4:11:06 mins
Python SEABORN Tutorial
Video - 3:58:27 mins
GIT Full Tutorial for Beginners
Video - 2:48:56 mins
Git and GitHub Tutorial for Beginners
Video - 1:14:17 mins
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.
Perceptron Explained: The Foundation of Neural Networks
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Perceptron Explained: The Foundation of Neural Networks 2
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MLP (Artificial Neural Network) Explained with Notation
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Feed Forward Propagation in Neural Networks Explained
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Feed Forward Propagation in Neural Networks Explained
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Backpropagation in Neural Networks
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Core Deep Learning Concepts – Quiz 1
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Core Deep Learning Concepts – Quiz 1
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Core Deep Learning Concepts – Quiz 1
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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.
How to Set Up PyTorch in VS Code & Google Colab (2026 Ultimate Guide)
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PyTorch Tensor Creation - torch.tensor(), torch.as_tensor(), torch.from_numpy() Complete Guide
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PyTorch Tensor Initialization: torch.zeros(), torch.ones(), and torch.empty() Explained
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torch.tensor() vs torch.Tensor() Explained — PyTorch Beginner Mistake!
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Tensor Initialization - torch.full(), torch.eye(), torch.diag() Methods Explained
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PyTorch Random Tensors Explained: torch.rand(), torch.randn(), torch.randint() Tutorial
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Advanced Random Functions - torch.randperm(), torch.multinomial(), torch.normal() Guide
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torch.zeros_like(), torch.ones_like(), torch.empty_like(), torch.full_like() Tutorial| Ali Hassan
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Random Like Functions - torch.rand_like(), torch.randn_like(), torch.randint_like() | Ali Hassan
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Sequence Generation - torch.arange(), torch.linspace(), torch.logspace() Tutorial| Ali Hassan
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Tensor Device Creation - Creating Tensors on CPU vs GPU with .to() and .cuda() , .cpu()| Ali Hassan
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Complex Tensors - torch.complex(), torch.polar(), torch.view_as_complex() Tutorial| Ali Hassan
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Memory Layout - torch.contiguous(), torch.is_contiguous(), memory_format Parameter | Ali Hassan
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Tensor Reshaping - .reshape()vs .view() vs .resize_() Complete Comparison| Ali Hassan
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Tensor Attributes - .dtype, .device, .requires_grad Properties Explained| Ali Hassan
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Advanced Tensor Creation in PyTorch – torch.stack(), torch.cat(), torch.chunk() | Ali Hassan
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Tensor Cloning in PyTorch – .clone(), .detach(), .copy_() Explained| Ali Hassan
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Tensor Conversion in PyTorch – .numpy(), .tolist(), .item() Methods| Ali Hassan
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Tensor Validation - torch.is_tensor(), torch.is_storage(), torch.is_complex() Functions| Ali Hassan
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Tensor Comparison PyTorch – torch.equal(), torch.allclose(), torch.isclose() Explained | Ali Hassan
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Tensor Info Functions in PyTorch – .shape, .size(), .dim(), .stride() Explained| Ali Hassan
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Tensor Utilities - torch.numel(), torch.element_size(), torch.storage_offset() Guide| Ali Hassan
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torch.set_default_dtype() – PyTorch's Global Floating-Point Precision Setter| Ali Hassan
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Tensor Memory - .storage(), .data_ptr(), .untyped_storage() Advanced Guide| Ali Hassan
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Broadcasting Tensors - torch.broadcast_tensors(), torch.broadcast_to() Tutorial| Ali Hassan
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Basic Arithmetic in PyTorch – .add(), .sub(), .mul(), .div() Operations| Ali Hassan
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Advanced Arithmetic in PyTorch – .addcdiv(), .addcmul(), .lerp() Functions| Ali Hassan
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Power Operations in PyTorch – .pow(), .sqrt(), .rsqrt(), .square() Complete Tutorial| Ali Hassan
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Exponential Functions - .exp(), .exp2(), .expm1(),Guide| Ali Hassan
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Logarithmic Operations in PyTorch – .log(), .log10(), .log2(), .log1p(), .logaddexp(), .logaddexp2()
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Trigonometric Functions in PyTorch – .sin(), .cos(), .tan(), .asin(), .acos(), .atan()| Ali Hassan
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Hyperbolic Functions in PyTorch – .sinh(), .cosh(), .tanh(), .asinh(), .acosh(), .atanh | Ali Hassan
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Rounding Operations - .round(), .floor(), .ceil(), .trunc(), .frac() Guide| Ali Hassan
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Sign and Absolute Functions in PyTorch – .abs(), .sign(), .signbit(), .copysign()| Ali Hassan
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Clamping Operations - .clamp(), .clamp_min(), .clamp_max() Complete Guide| Ali Hassan
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Remainder Operations in PyTorch – .remainder(), .fmod(),Tutorial| Ali Hassan
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Comparison Operations in PyTorch- .eq(), .ne(), .lt(), .le(), .gt(), .ge() Guide| Ali Hassan
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Logical Operations in PyTorch- .logical_and(), .logical_or(), .logical_not, .logical_xor| Ali Hassan
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Finite Checks in PyTorch- .isfinite(), .isinf(), .isnan(), .isneginf(), .isposinf() | Ali Hassan
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Type Checking Functions in PyTorch- .is_complex(), .is_floating_point(), .is_signed() | Ali Hassan
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Precision Control in PyTorch- .half(), .float(), .double(), precision conversion| Ali Hassan
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Dimension Manipulation in PyTorch - .squeeze(), .unsqueeze(), .flatten() Tutorial| Ali Hassan
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Tensor Transposition in PyTorch - .transpose(), .t(), .permute() Methods Explained | Ali Hassan
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Tensor Expansion in PyTorch - .expand(), .expand_as(), .repeat() Complete Guide| Ali Hassan
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Advanced Tensor Expansion in PyTorch - .repeat_interleave(), .tile(), broadcasting | Ali Hassan
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Tensor Slicing in PyTorch - .narrow(), .select(), .slice() Methods Tutorial| Ali Hassan
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Tensor Splitting in PyTorch - torch.split(), torch.chunk(), torch.tensor_split() | Ali Hassan
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Tensor Uniqueness in PyTorch - torch.unique(), torch.unique_consecutive() Tutorial| Ali Hassan
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Tensor Unfolding, Folding in PyTorch - .unflatten(), torch.nn.Unfold(),torch.nn.fold()| Ali Hassan
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PyTorch Tensor Indexing – select(), take(), fill(), copy(), and put() Explained| Ali Hassan
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Advanced Indexing in PyTorch – .gather(), .scatter(), .scatter_add(), torch.index_add()| Ali Hassan
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Tensor Masking in PyTorch - .masked_select(), .masked_fill(), .masked_scatter() Guide| Ali Hassan
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Conditional Operations in PyTorch – torch.where(), .masked_fill_()| Ali Hassan
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Tensor Padding in PyTorch – torch.nn.functional.pad() and Padding Modes Explained | Ali Hassan
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Tensor Sorting in PyTorch – torch.sort(), torch.argsort(), and torch.topk() Explained| Ali Hassan
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Tensor Flipping in PyTorch – torch.flip(), torch.fliplr(), torch.flipud() Tutorial| Ali Hassan
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Tensor Rolling in PyTorch – torch.roll(), torch.rot90() explained| Ali Hassan
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Sum Operations in PyTorch – .sum(), .nansum(), .cumsum(), .cumprod() ,keepdim| Ali Hassan
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PyTorch Mean Operations Explained – .mean(), .nanmean(), .median(), .nanmedian() | Ali Hassan
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PyTorch Statistics Operations Explained – .std(), .var(), .std_mean(), .var_mean() | Ali Hassan
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PyTorch Min/Max Operations – .min(), .max(), .aminmax(), torch.amin(), torch.amax()| Ali Hassan
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PyTorch Index-Finding Operations – .argmin(), .argmax(), .mode(), torch.argwhere()| Ali Hassan
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PyTorch Product Operations Explained – .prod() Function Tutorial| Ali Hassan
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PyTorch Matrix Trace Operations – .trace() and .diagonal().sum() Explained| Ali Hassan
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PyTorch Triangular Matrices Explained – .tril() and .triu() Tutorial| Ali Hassan
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PyTorch Matrix Multiplication Explained – torch.mm(), torch.matmul(), torch.bmm(), @, .mv(), .ger()
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PyTorch Linear Layers Explained – nn.Linear(), nn.LazyLinear(), nn.Bilinear() Tutorial | Ali Hassan
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PyTorch Fundamentals Quiz
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PyTorch Fundamentals Quiz
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PyTorch Fundamentals Quiz
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Hands-On Project: Linear Regression with 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.
Mathematics for Artificial Intelligence
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.
Lecture 1: Math for AI (Artificial Intelligence)– Real, Rational, Complex, Logarithms,Exponents
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Lecture 2: Math for AI (Artificial Intelligence)– Set Theory, Mathematical Logic
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Lecture 3 : Matrix Theory & Linear Systems | From Basics to AI Applications
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Lecture 4 : Sequences, Series, Factorials, Permutations, Combinations & Binomial Theorem Explained
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Lecture 5: Complete Geometry and Mensuration: From Basic Points to 3D Shapes
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Mathematics for AI
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Mathematics for AI
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Mathematics for AI
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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.
Linear Activation Function Explained || Why Use a Linear Activation Function?
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Coding the Linear Activation Function in PyTorch: Step-by-Step Guide
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Threshold Activation Function Explained || Why Use a Threshold Activation Function?
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Coding the Threshold Activation Function in PyTorch: Step-by-Step Guide
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Sigmoid Activation Function Explained & It's Derivative || Why Use a Sigmoid Activation Function?
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Coding the Sigmoid Activation Function in PyTorch: Step-by-Step Guide
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Tanh Activation Function Explained & It's Derivative || Why Use a Tanh Activation Function?
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Coding the Tanh Activation Function in PyTorch: Step-by-Step Guide
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Softmax Activation Function Explained & It's Derivative || Why Use a Softmax Activation Function?
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Coding the Softmax Activation Function in PyTorch: Step-by-Step Guide
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Rectified Linear Unit (ReLU) Activation Function Explained & It's Derivative
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Coding the Rectified Linear Unit (ReLU) Activation Function in PyTorch: Step-by-Step Guide
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Leaky ReLU (Leaky Rectified Linear Unit) Activation Function Explained & It's Derivative
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Coding the Leaky ReLU (Leaky Rectified Linear ) Activation Function in PyTorch: Step-by-Step Guide
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Parametric ReLU (PReLU) Activation Function Explained & It's Derivative
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Coding the Parametric ReLU (PReLU) Activation Function in PyTorch: Step-by-Step Guide
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Exponential Linear Unit (ELU) Activation Function Explained & It's Derivative
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Coding the Exponential Linear Unit (ELU) Activation Function in PyTorch: Step-by-Step Guide
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Scaled Exponential Linear Unit (SELU) Activation Function Explained & It's Derivative
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Coding the Scaled Exponential Linear Unit (SELU) Activation Function in PyTorch: Step-by-Step Guide
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Swish Activation Function Explained & It's Derivative
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Coding the Swish Activation Function in PyTorch: Step-by-Step Guide
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Softplus Activation Function Explained & It's Derivative
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Coding the Softplus Activation Function in PyTorch: Step-by-Step Guide
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Mish Activation Function Explained & It's Derivative
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Coding the Mish Activation Function in PyTorch: Step-by-Step Guide
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All Activation Functions in Deep Learning – Explained with PyTorch
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All Activation Functions in Deep Learning – Explained with PyTorch
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All Activation Functions in Deep Learning – Explained with PyTorch
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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 Loss(Cost) Functions Explained || Loss Function vs Cost Function| Convex and non Convex loss
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Mean Squared Error (MSE)(L2 LOSS) Cost Function Explained & It's Derivative
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Coding the Mean Squared Error (MSE)(L2 LOSS) Cost Function in PyTorch: Step-by-Step Guide
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Mean Absolute Error (MAE)(L1 Loss) Cost Function Explained & It's Derivative
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Coding the Mean Absolute Error (MAE)(L1 Loss) Cost Function in PyTorch: Step-by-Step Guide
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Mean Bias Error (MBE) Cost Function Explained & It's Derivative
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Coding the Mean Bias Error (MBE) Cost Function in PyTorch: Step-by-Step Guide
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Root Mean Squared Error (RMSE) Cost Function Explained & It's Derivative
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Coding the Root Mean Squared Error (RMSE) Cost Function in PyTorch: Step-by-Step Guide
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Root Mean Squared Logarithmic Error (RMSLE) Cost Function Explained & It's Derivative
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Root Mean Squared Logarithmic Error (RMSLE) Cost Function in PyTorch: Step-by-Step Guide
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Huber Loss(Smooth L1 Loss) Cost Function Explained & It's Derivative
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Huber Loss(Smooth L1 Loss) Cost Function in PyTorch: Step-by-Step Guide
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Log-Cosh Loss Cost Function Explained & It's Derivative
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Log-Cosh Loss Cost Function in PyTorch: Step-by-Step Guide
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Binary Cross-Entropy Loss (BCE)(logloss) Cost Function Explained & It's Derivative
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Binary Cross-Entropy Loss (BCE)(logloss) Cost Function in PyTorch: Step-by-Step Guide
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Categorical Cross-Entropy Loss Cost Function Explained & It's Derivative
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Coding Categorical Cross-Entropy Loss Cost Function in PyTorch: Step-by-Step Guide
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All Loss & Cost Functions in Deep Learning – Explained with PyTorch
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All Loss & Cost Functions in Deep Learning – Explained with PyTorch
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All Loss & Cost Functions in Deep Learning – Explained with PyTorch
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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.
Batch Gradient Descent Explained || Stochastic Gradient Descent (SGD)|| Mini-batch Gradient Descent
Video - 1:55:14 mins
Coding Batch,SGD,Mini,Gradient Descent in PyTorch: Step-by-Step Guide
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Exponentially Weighted Moving Average (EWMA) Explained || Understanding EWMA and Its Applications
Video - 00:44:13 mins
SGD with Momentum Explained || Boosting Gradient Descent with Momentum
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Coding SGD with Momentum Optimizer in PyTorch: Step-by-Step Guide
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Nesterov Accelerated Gradient (NAG) Optimizer Explained & It's Derivative
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Coding Nesterov Accelerated Gradient (NAG) Optimizer in PyTorch: Step-by-Step Guide
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AdaGrad & RMSProp Optimizers Explained & It's Derivative
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Coding AdaGrad & RMSProp Optimizer in PyTorch: Step-by-Step Guide
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Adam (Adaptive Moment Estimation) Optimizers Explained & It's Derivative
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Coding Adam (Adaptive Moment Estimation) Optimizer in PyTorch: Step-by-Step Guide
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All Optimizers in Deep Learning – Explained with PyTorch
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All Optimizers in Deep Learning – Explained with PyTorch
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All Optimizers in Deep Learning – Explained with PyTorch
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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.
Classification With Neural Networks with PyTorch: Hands-On Project for Beginners
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Hands-On Project: Classification with Neural Networks in PyTorch
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Hands-On Project: Classification with Neural Networks in PyTorch
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Hands-On Project: Classification with Neural Networks in PyTorch
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Improving Neural Network Performance
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.
Vanishing & Exploding Gradients Explained || Why Do Gradients Vanish or Explode?
Video - 00:18:06 mins
Overfitting & Underfitting Explained || Why Do Models Overfit or Underfit?
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Regularization in Deep Learning || L1, L2, and Elastic Net Explained!
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Coding Regularization in PyTorch || L1, L2, and Elastic Net
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Dropout in Deep Learning Explained || Preventing Overfitting in Neural Networks!
Video - 00:8:32 mins
All Normalizations Explained: Batch, Layer, Instance, Group, RMS
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Coding All Normalizations Explained With Pytorch : Batch, Layer, Instance, Group, RMS
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Improving Neural Network Performance
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Improving Neural Network Performance
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Improving Neural Network Performance
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Natural Language Processing (NLP) – Sentiment Analysis, LSTM & Seq2Seq 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.
must watch
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Introduction to NLP
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End to End NLP Pipeline
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Text Preprocessing
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Text Representation
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Word2vec
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Hands-On Project: Sentiment Analysis with Word Embeddings in PyTorch
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Hands-On Project: Sentiment Analysis with LSTM in PyTorch
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Hands-On Project: Build a Mini Google Translate using Seq2Seq in PyTorch
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Natural Language Processing (NLP) – Sentiment Analysis, LSTM & Seq2Seq Models
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Natural Language Processing (NLP) – Sentiment Analysis, LSTM & Seq2Seq Models
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Natural Language Processing (NLP) – Sentiment Analysis, LSTM & Seq2Seq Models
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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.
Coding Transformer From Scratch With Pytorch
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Coding Transformer From Scratch With Pytorch || Part 2
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Implementing Transformers from Scratch in PyTorch
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Implementing Transformers from Scratch in PyTorch
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Implementing Transformers from Scratch in PyTorch
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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.
Convolutional Layers: nn.Conv2d, Filters, Padding, Kernels, and Image Types (Grayscale & RGB) CV 001
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Image Classification with Logistic Regression in PyTorch | Beginner Friendly Tutorial | Ali Hassan
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Image Classification with MLP in PyTorch – Step-by-Step Tutorial| Ali Hassan
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Master PyTorch Conv2d: Filters, Edge Detection, LazyConv2d & Output Size Formula CV 002 | Ali Hassan
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Pooling Layers in PyTorch Explained – MaxPool2d, AvgPool2d, AdaptiveMaxPool2d & AdaptiveAvgPool2d
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LeNet-5 from Scratch in PyTorch – Image Classification LeNet-5 in PyTorch | Ali Hassan
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All Weight Initialization Techniques | Xavier, He, LeCun, Kaiming, Glorot, Uniform, Normal
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PyTorch Min–Max Normalization | Scale Images & Feature Maps to [0,1] (with Code)| Ali Hassan
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PyTorch nn.Sequential Explained | Build Neural Networks Step by Step| Ali Hassan
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Learning Rate Finder in PyTorch | Best LR & Exponential Learning Rate Scheduler Explained|Ali Hassan
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AlexNet from Scratch in PyTorch | Image Classification Tutorial | Ali Hassan
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VGGNet from Scratch in PyTorch | VGG11, VGG13, VGG16, VGG19 for Image Classification | Ali Hassan
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ResNet from scratch in PyTorch | ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
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Computer Vision with PyTorch: From Fundamentals to Modern Architectures
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Computer Vision with PyTorch: From Fundamentals to Modern Architectures
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Computer Vision with PyTorch: From Fundamentals to Modern Architectures
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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.
Coding Vision Transformer (ViT) From Scratch With Pytorch
Video - 6:52:59 mins
Vision Transformer (ViT): From Research Paper to PyTorch Implementation
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Vision Transformer (ViT): From Research Paper to PyTorch Implementation
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Vision Transformer (ViT): From Research Paper to PyTorch Implementation
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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.
Hands-On Project: Building a Generative Model from Scratch with PyTorch
Video - 2:22:18 mins
Hands-On Project: Image Generation from Scratch with Deep Convolutional Generative Models (PyTorch)
Video - 2:20:41 mins
Least_Squares_GAN
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WGAN: Wasserstein Generative Adversarial Networks
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Generative Models – From Fundamentals to Advanced Architectures
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Generative Models – From Fundamentals to Advanced Architectures
Quiz
Generative Models – From Fundamentals to Advanced Architectures
Quiz
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
U-Net Image Segmentation Project From Scratch in PyTorch
Coding Swin Transformer from Scratch in PyTorch
Coding Swin Transformer from Scratch in PyTorch part 1
Video - 00:50:17 mins
Coding Swin Transformer from Scratch in PyTorch part 2
Video - 00:21:25 mins
Coding Swin Transformer from Scratch in PyTorch part 3
Video - 1:30:30 mins
Coding Swin Transformer from Scratch in PyTorch part 4
Video - 01:19:08 mins
Coding Large Language Models (ChatGPT, Llama & GPT) From Scratch Using PyTorch
Introduction of Large Language Models
Video - 00:4:07 mins
Base OF Large Language Models
Video - 11:46:24 mins
Base OF Large Language Models part 2
Video - 00:59:36 mins
Normalizations in Large Language Models
Video - 00:5:00 mins
Normalizations in Large Language Models part 2
Video - 02:57:00 mins
Normalizations in Large Language Models part 3
Video - 00:14:43 mins
Weight Initialization Techniques in Large Language Models
Video - 00:5:00 mins
Weight Initialization Techniques in Large Language Models part 2
Video - 01:26:00 mins
Coding ChatGPT, GPT From Scratch Using PyTorch
Video - 1:37:47 mins
Coding Llama 2 From Scratch Using PyTorch
Video - 00:14:25 mins
RMS Normalization (RMSNorm) in Llama 2
Video - 00:5:00 mins
Rotary Positional Embeddings (RoPE) in Llama 2
Video - 1:04:09 mins
Grouped Query Attention in Llama 2
Video - 00:41:41 mins
Text Generation With Llama 2
Video - 00:38:36 mins
Coding Stable Diffusion From Scratch Using PyTorch
Understanding Stable Diffusion Architecture
Video - 00:12:44 mins
Cross and Self-Attention in Stable Diffusion
Video - 00:22:06 mins
Clip Text Encoder in Stable Diffusion
Video - 00:16:03 mins
Variational Autoencoder (VAE) in Stable Diffusion
Video - 1:04:58 mins
U-Net and Diffusion model in Stable Diffusion
Video - 00:45:09 mins
DDPMScheduler in Stable Diffusion
Video - 2:05:05 mins
stable-diffusion training ,inference (pytorch)
Video - 01:28:52 mins
Coding Multimodal(Vision) PaliGemma From Scratch Using PyTorch
PaliGemma architecture
Video - 00:7:07 mins
Imports of PaliGemma
Video - 00:7:09 mins
SigLIP Vision Encoder (Vision Part) of PaliGemma
Video - 00:40:11 mins
Image Processing and PaliGemmaProcessor
Video - 00:23:07 mins
Building the Gemma Language Model from Scratch | Config, RMSNorm, RoPE & Attention
Video - 00:46:53 mins
Building KV Cache from Scratch in PyTorch | Gemma LLM
Video - 00:43:18 mins
Building PaliGemma from Scratch | Multimodal Fusion & Forward Pass
Video - 00:58:5 mins
PaliGemma Dataset,Tokenizer,Training & Inference
Video - 01:38:06 mins
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