BSCS3004 · Knowledge Base
Deep Learning Notes
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Procedures
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34 - 🧬 Deep Learning History & The Perceptron
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35 - ⚡ Activation Functions
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36 - 🔄 Perceptron to Multi-Layer Perceptron
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37 - ➡️ Forward Propagation & Loss Functions
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38 - 🔙 Backpropagation
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39 - ⚙️ Optimization Methods
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40 - 🛡️ Regularization
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41 - 🔢 CNN Operations Deep Dive
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42 - 🖼️ Convolutional Neural Networks
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43 - 🏗️ CNN Architectures & Transfer Learning
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44 - 🧱 ResNet & Skip Connections
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45 - 🔄 RNN Variants- GRU, Bidirectional, Deep RNNs
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46 - 🔬 LSTM & GRU Detailed Analysis
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47 - 🏗️ Transformers & Attention
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48 - 🔄 Seq2Seq & Attention Mechanisms
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49 - 🏗️ Transformer Architecture Deep Dive
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50 - ⚙️ Optimizers Comparison
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51 - 📊 Batch Normalization- Training & Inference
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52 - 🔄 Recurrent Neural Networks & LSTMs
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