5m ago · 1 min read · Understand how models make decisions: feature importance, SHAP values, LIME explanations. Why XAI matters: compliance (GDPR right to explanation), fairness, debugging. Visualizations: decision plots, force plots. Python implementation, interpreting b...
Join discussion5m ago · 1 min read · Deploy deep learning on smartphones, IoT, microcontrollers: quantization, pruning, knowledge distillation. TensorFlow Lite, ONNX, TVM. Trade-off: accuracy vs latency vs memory. Use cases: gesture recognition, anomaly detection, predictive maintenance...
Join discussion5m ago · 1 min read · Master modern transfer learning: BERT for understanding, GPT for generation. Fine-tuning, prompt engineering, retrieval-augmented generation (RAG). When to use BERT vs GPT vs open-source (Llama, Mistral). Hugging Face ecosystem: model hub, tokenizers...
Join discussion5m ago · 1 min read · Learn how agents learn from rewards: Markov Decision Processes, Q-learning, Deep Q-Networks (DQN), Policy Gradient, Proximal Policy Optimization (PPO). Applications: games, robotics, trading. Implementation with Gymnasium (OpenAI). Demo: training age...
Join discussion5m ago · 1 min read · Understand the models that generate DALL-E and Stable Diffusion images: diffusion process (noise → data), reverse process (noise → image). Conditioning: text-to-image, inpainting. Why better than GAN (stable training, quality). Hugging Face Diffusers...
Join discussion5m ago · 1 min read · Discover how GANs generate realistic data: two networks in competition (generator vs discriminator). Applications: synthetic images, data augmentation, domain transfer. Challenges: training instability, mode collapse. Variants: Conditional GAN, Cycle...
Join discussion5m ago · 1 min read · Understand the architecture behind GPT and BERT: self-attention, multi-head attention, positional encoding. Transformer block: encoder-decoder. Why attention is better than RNN (parallelization, long-range dependencies). Fine-tuning BERT for NLP task...
Join discussion5m ago · 1 min read · Learn deep learning for sequences: RNNs process sequences while maintaining memory. LSTMs solve vanishing gradient. GRUs as lightweight alternative. Use cases: time series forecasting, sentiment analysis, machine translation (before Transformers). Py...
Join discussion6m ago · 1 min read · Master computer vision: discover how CNNs capture visual patterns. Convolution, pooling, feature maps. Classic architectures (AlexNet, VGG, ResNet) and modern (EfficientNet, Vision Transformer). Transfer learning from ImageNet. End-to-end PyTorch imp...
Join discussion6m ago · 1 min read · Learn how neural networks work: from the perceptron (1958) to modern deep neural networks. Concepts: neurons, weights, backpropagation, activation functions, layer architecture. Understand the \ What you'll learn Sigmoid ReLU (Rectified Linear Unit)...
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