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Scientific Python
For Telecommunications and Computational Imaging
From rusty Python fundamentals to GPU-accelerated solvers and large language models. The practical toolkit for implementing everything in Books 1 and 2.
52 chapters427 sections122+ hours of content
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Python Language Deep DiveScientific Computing with NumPy and SciPyGPU Computing: CuPy and PyTorch TensorsVisualization and Data PresentationSignal Processing and Communications in PythonDeep Learning with PyTorchLarge Language Models and Advanced MLInverse Problems and ReconstructionSoftware Engineering for ResearchCapstone ProjectsFuture topics
All Chapters
Part 1: Python Language Deep Dive
Part 2: Scientific Computing with NumPy and SciPy
Part 3: GPU Computing: CuPy and PyTorch Tensors
Chapter 10
GPU Computing Fundamentals
~120 minIntermediate
Chapter 11
CuPy โ NumPy on the GPU
~150 minIntermediate
Chapter 12
PyTorch Tensors as a Scientific Computing Tool
~150 minIntermediate
Chapter 13
Performance Patterns and Memory Management
~120 minIntermediate
Chapter 14
Accelerating Python Beyond GPU
~120 minIntermediate
Part 4: Visualization and Data Presentation
Chapter 15
Matplotlib โ From Quick Plots to Publication Quality
~150 minFoundational
Chapter 16
Advanced 2D Visualization
~120 minIntermediate
Chapter 17
3D Visualization
~150 minIntermediate
Chapter 18
LaTeX Integration and Automated Reporting
~90 minIntermediate
Chapter 19
Jupyter, Pandas, and Interactive Development
~120 minFoundational
Part 5: Signal Processing and Communications in Python
Chapter 20
Digital Modulation and Detection
~120 minIntermediate
Chapter 21
Channel Modeling and Fading Simulation
~120 minIntermediate
Chapter 22
OFDM System Simulation
~150 minIntermediate
Chapter 23
MIMO Detection and Precoding
~150 minIntermediate
Chapter 24
Beamforming and Array Processing
~120 minIntermediate
Chapter 25
Radar Signal Processing Implementation
~120 minIntermediate
Part 6: Deep Learning with PyTorch
Chapter 26
PyTorch Neural Network Fundamentals
~150 minIntermediate
Chapter 27
Convolutional Neural Networks
~150 minIntermediate
Chapter 28
Working with Complex-Valued Data in Neural Networks
~120 minAdvanced
Chapter 29
Recurrent Networks and Sequence Models
~120 minIntermediate
Chapter 30
Attention and Transformer Architectures
~150 minIntermediate
Chapter 31
Generative Models โ Implementation
~180 minAdvanced
Chapter 32
Reinforcement Learning Foundations
~150 minAdvanced
Chapter 33
Transfer Learning, Pre-Trained Models, and Fine-Tuning
~120 minIntermediate
Part 7: Large Language Models and Advanced ML
Chapter 34
Natural Language Processing Foundations
~120 minIntermediate
Chapter 35
How Large Language Models Work
~150 minAdvanced
Chapter 36
Using LLMs Programmatically
~150 minIntermediate
Chapter 37
Fine-Tuning and Training Language Models
~150 minAdvanced
Chapter 38
LLMs for Telecommunications and Imaging Research
~150 minAdvanced
Chapter 39
Advanced ML Topics
~180 minAdvanced