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Optimizers >Logistic Regression
Course content
Artificial Intelligence Bundle - Computer Vision Special
Blog Link Free0 s
Introduction Free19 m 1 s- Introduction - 2 Free11 m 32 s
- PCA - 1 Free3 m 28 s
- PCA - 2 Free13 m 48 s
- PCA - 3 Free12 m 13 s
- PCA - 4 Free9 m 49 s
- Eigen value and vector Free22 m 59 s
- Dimensionality Reduction Free9 m 58 s
- Visualize MNIST Dataset Free4 m 47 s
- PCA Code Free0 s
- PCA for dimensionality reduction Free14 m 44 s
- t-SNE Free6 m 25 s
- Embedding Free6 m 25 s
- t-SNE (continue)8 m 8 s
- Crowding Problem7 m 50 s
- Apply t-SNE37 m 56 s
- t-SNE on MNIST6 m 14 s
- Code Example8 m 2 s
PROJECT - Face Recognition (Code )0 s
- History Free24 m 23 s
- Biological Neuron Free8 m
- Biological Neuron - 2 Free16 m 7 s
- Logistic Regression Free16 m 40 s
- MLP Free0 s
- Notation17 m 51 s
- Single neuron training27 m 53 s
- MLP Chain Rule39 m 57 s
- MLP Memoization13 m 26 s
- Backpropagation25 m 21 s
- Activation Functions16 m 58 s
- Vanishing Gradient22 m 17 s
- Bias Variance9 m 52 s
- Decision surfaces Playground14 m 17 s
- MLP 1980s15 m 40 s
- Dropout and Regularization20 m 8 s
- ReLU27 m 17 s
- Weight Intialization23 m 44 s
- Batch Normalization20 m 7 s
- Optimizer -10 s
- Optimizer -212 m 49 s
- SGD - 10 s
- SGD - 224 m 51 s
- NAS7 m 35 s
- AdaGrad14 m 58 s
- AdaDelta and RMSProp9 m 16 s
- Adam10 m 29 s
- Which one to choose5 m 2 s
- Gradient Checking9 m 40 s
- Softmax and Cross-Entropy24 m 22 s
- Deep MLP Training7 m 38 s
- Auto Encoders26 m 21 s
- Word2Vec CBOW18 m 16 s
- Word2Vec Skip gram13 m 48 s
- Word2Vec Algorithmic Optimization11 m 27 s
- Tensorflow and Keras - Introduction22 m 28 s
- GPU vs CPU23 m 4 s
- Optimizer Choice0 s
- Introduction17 m 4 s
- Edge Detection Free27 m 48 s
- Padding and Stride18 m 34 s
- RGB Convolution10 m 46 s
- Layers23 m 2 s
- Max Pool11 m 49 s
- Optimization8 m 35 s
- LeNet9 m 42 s
- ImageNet Dataset5 m 53 s
- Augementation7 m 6 s
- Convolution Layers16 m 43 s
- AlexNet12 m 56 s
- VGGNet10 m 32 s
- Residual Network21 m 18 s
- Inception Network18 m 7 s
- Transfer Learning22 m 47 s
- Cat Vs Dog14 m 15 s
- MNIST5 m 9 s
- References for resources0 s
- PROJECT - Github Repo Link (CODE)0 s
- Problem Definition Free13 m 34 s
- Dataset Free8 m 30 s
- Data Analysis4 m 17 s
- Dash-cam images and steering angles5 m 22 s
- Split the dataset Train vs Test2 m 53 s
- EDA Steering angles6 m 4 s
- Mean Baseline model simple4 m 25 s
- Deep Learning for regression CNN, CNN+RNN9 m 18 s
- Batch load the dataset5 m 53 s
- NVIDIA’s end to end CNN model17 m 48 s
- Train the model12 m 51 s
- Test and visualize the output Free10 m 57 s