Neural Networks with Python : 1

Neural Networks with Python & PyTorch: From Perceptrons to Transformers

What you'll learn
- Understand and implement a wide range of neural network architectures including MLP, CNN, RNN, LSTM, and GAN.
- Code neural networks from scratch using NumPy, then build scalable models using PyTorch or TensorFlow.
- Apply deep and shallow neural networks to real tasks like classification, regression, sequence modeling, and image generation.
- Train and optimize networks using gradient descent, dropout, batch normalization, and different learning rate schedules.
- Visualize model outputs, decision boundaries, and internal representations to understand network behavior.

Requirements
- Some knowledge of linear algebra (matrices, dot product), and functions like sigmoid or softmax is helpful

Description
This course is designed to give you a clear and practical understanding of neural networks, starting from the most basic concepts and building up to advanced architectures used in research and industry today. We begin with perceptrons and multilayer perceptrons, the foundation of neural network models. From there, we move step by step into training fundamentals such as weight initialization methods (Xavier and He), loss functions, and optimization strategies. Regularization techniques like dropout and batch normalization are also covered to help you understand how to improve model performance and reduce overfitting.

Once the fundamentals are in place, we expand into deep feedforward networks, residual connections, and convolutional neural networks (CNNs). You will see how CNNs are applied both in theory and in practice with PyTorch, as well as how similar architectures can be implemented in Julia and MATLAB. The course then progresses into recurrent neural networks (RNNs), LSTMs, GRUs, and temporal models, preparing you to handle sequence data and forecasting problems.

In later sections, we cover attention mechanisms and transformers, which are now standard tools in natural language processing and computer vision. We also explore autoencoders, variational autoencoders, probabilistic models such as Bayesian neural networks, and self-organizing approaches like Kohonen networks. The course includes topics on graph neural networks (GNNs) and other specialized architectures like echo state networks and neural ODEs, ensuring you gain exposure to a wide range of techniques.

Throughout the course, the focus remains on both intuition and application. You will see mathematical formulas explained step by step, and then see how they are implemented in code. By the end of this course, you will not only understand how neural networks are built and trained but also be able to experiment with them confidently in your own projects.

Who this course is for:
- Machine learning enthusiasts who want to build models from scratch.
- Data science students seeking a solid grasp of both shallow and deep neural networks.
- Engineers or developers looking to apply deep learning to real-world problems.
- Anyone who finds typical deep learning tutorials too shallow or too reliant on libraries and wants to truly understand how models work internally.
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