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Featured learning series
Start with complete tracks and go deep in a predictable order.
1
Computer Vision Roadmap: From Basics to Real Projects
2 What is Computer Vision? From Pixels to Decisions
3 A Short History of Computer Vision: 1545 to Now
4 Math for CV Beginners: Vectors, Matrices, Convolutions
5 Image Fundamentals: Color, Histograms, Noise, Filtering
6 How Images and Video Are Stored: Colour, JPEG, Frames
7 OpenCV Setup + First 10 Tasks in Python
8 Vision Metrics: Accuracy, Precision, Recall, mAP, IoU
9 CV Project Workflow: Dataset, Baseline, Error Iteration
10 Intensity Transforms and Frequency-Domain Filtering
11 Edge Detection and Thresholding That Actually Work
12 Morphology, Contours, and Shape Analysis for Real Images
13 Feature Matching (SIFT/ORB) and Image Stitching
14 Camera Calibration and Perspective Correction
15 Epipolar Geometry, Stereo Vision, and Depth Estimation
16 Optical Flow and Motion Tracking in Video
17 HOG, HOF and MBH: Descriptors Before Deep Learning
18 Your First Image Classifier (PyTorch + Transfer Learning)
19 Data Pipelines and Augmentation for Vision Models
20 CNN Architectures Explained: From LeNet to ResNet
21 YOLO Detection Pipeline: Data to Inference
22 Semantic and Instance Segmentation: U-Net to Mask R-CNN
23 Vision Transformers (ViT) and When to Use Them
24 CV Explainability: Grad-CAM, Failures, Bias Checks
25 3D Vision Basics: SfM, Point Clouds, and Pose Estimation
26 Multimodal Vision: CLIP, Embeddings, and Retrieval Systems
27 Video Understanding: Flow Networks, Interpolation, Stabilisation
28 Real-Time CV Systems: Tracking, Latency, Streaming
29 Deploying CV Models: ONNX, TensorRT, Edge, and APIs
30 Responsible CV: Privacy, Fairness, Security, Governance
1
GPUs: from pixels to parallel supercomputers
2 Your first CUDA program: kernels, threads, and grids
3 Thread hierarchy in CUDA: threads, blocks, warps, and grids
4 Warp divergence in CUDA: detection and optimization
5 CUDA memory hierarchy: where your data lives matters
6 Memory coalescing: the most important optimization you will learn
7 Shared memory and tiling: the key to fast matrix operations
8 Debugging and profiling CUDA programs
9 Device functions, host functions, and CUDA function qualifiers
10 CUDA synchronization and atomics: __syncthreads, atomicAdd, and barriers
11 Parallel prefix sum and reduction: the core parallel primitives
12 Concurrent data structures on the GPU
13 CUDA streams and asynchronous execution
14 cudaEventSynchronize and CUDA events: accurate kernel timing
15 Dynamic parallelism: kernels launching kernels
16 Unified virtual memory: one pointer for CPU and GPU
17 Multi GPU CUDA: NCCL, NVLink, and peer access
18 Memory allocation patterns and multi-dimensional arrays in CUDA
19 Texture and constant memory: specialized caches
20 CUDA occupancy and register pressure: performance tuning guide
21 Case study: matrix multiplication from naive to cuBLAS speed
22 Case study: implementing a convolution layer in CUDA
23 Case study: reduction and histogram at scale
24 Heterogeneous computing: CPU and GPU working together
25 Advanced memory patterns: pinned memory, zero-copy, and more
26 Advanced stream patterns and concurrent kernel execution
27 Performance case studies and optimization patterns
28 Where to go from here: CUDA ecosystem and next steps
1
Neural networks explained: neurons, activations, and layers
2 Backpropagation: forward pass, chain rule, and gradient flow
3 Training neural networks: initialization, learning rates, and schedules
4 CNNs explained: convolution, pooling, and feature maps
5 RNNs and LSTMs: sequence modeling and gating mechanisms
6 Attention and transformers: self-attention, multi-head, and positional encoding
7 Word embeddings: Word2Vec, GloVe, and FastText explained
8 Transfer learning and fine-tuning: from ImageNet to your task
9 DNN optimization: momentum, Adam, and learning rate schedules
10 Deep learning regularization: dropout, batch norm, and data augmentation
11 Encoder-decoder architecture: seq2seq and U-Net explained
12 Generative models overview: autoregressive, flow-based, and latent variable
13 RBMs explained: energy-based models and contrastive divergence
14 Deep Belief Networks: greedy layer-wise pretraining explained
15 VAE explained: ELBO, reparameterization trick, and KL divergence
16 GANs explained: minimax game, mode collapse, and Wasserstein distance
17 DCGAN, conditional GANs, CycleGAN, and StyleGAN explained
18 Representation learning: autoencoders, contrastive learning, and SimCLR
19 Domain adaptation: covariate shift, DANN, and few-shot learning
20 Distributed representations: latent spaces and embedding geometry
21 AutoML: grid search, Bayesian optimization, and Hyperband
22 Neural Architecture Search: reinforcement learning, evolution, and DARTS
23 Model compression: pruning, quantization, and knowledge distillation
24 Graph Neural Networks: GCN, GraphSAGE, and GAT explained
25 Debugging deep learning: loss curves, gradient health, and ablations
26 ResNet architecture explained: skip connections and residual learning
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