Elnur Mammadli Email & Phone Number

Baku, AZ

Баку, Баку, Азербайджан

RetinaNet, Inception based Faster-RCNN and Mask-RCNN s, Class Activation Map, Saliency map.

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Practical aspects of Deep Learning-Train / Dev / Test sets-Bias / Variance-Basic Recipe for Machine Learning-Regularization-Why regularization reduces overfitting?-Dropout Regularization-Understanding Dropout-Other regularization methods-Normalizing inputs-Vanishing / Exploding gradients-Weight Initialization for Deep Networks-Numerical approximation of.

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Foundations of Convolutional Neural Networks-Computer Vision-Edge Detection Example-More Edge Detection-Padding-Strided Convolutions-Convolutions Over Volume-One Layer of a Convolutional Network-Simple Convolutional Network Example-Pooling Layers-CNN Example-Why Convolutions?Practice work:-Strided convolution-Simple Convolutional Network Example-CNN.

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ML Strategy (1)-Why ML Strategy-Orthogonalization-Single number evaluation metric-Satisficing and Optimizing metric-Train/dev/test distributions-Size of the dev and test sets-When to change dev/test sets and metrics-Why human-level performance?-Avoidable bias-Understanding human-level performance-Surpassing human-level performance-Improving your model.

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Recurrent Neural Networks-Why sequence models?-Recurrent Neural Network Model-Backpropagation through time-Different types of RNNs-Language model and sequence generation-Sampling novel sequences-Vanishing gradients with RNNs-Gated Recurrent Unit (GRU)-Long Short Term Memory (LSTM)-Bidirectional RNN-Deep RNNsPractical Work:-Gated Recurrent Unit (GRU)-Long.

Sentiment in text:-Word based encodings-Using APIs-Text to sequence-Looking more at the Tokenizer-Padding-Sarcasm, really?-Working with the TokenizerWord Embeddings:-The IMBD dataset1мин-Looking into the details-How can we use vectors?-More into the details-Notebook for lesson -Remember the sarcasm dataset?-Building a classifier for the sarcasm.

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Neural Networks Basics:-Binary Classification-Logistic Regression -Logistic Regression Cost Function -Gradient Descent-Derivatives -More Derivative Examples -Computation graph -Derivatives with a Computation Graph -Logistic Regression Gradient Descent -Gradient Descent on m Examples -Vectorization -Vectorizing Logistic Regression -Vectorizing Logistic.

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A New Programming Paradigm:-A primer in machine learning-The ‘Hello World’ of neural networks-Working through ‘Hello World’ in TensorFlow and PythonIntroduction to Computer Vision:-An Introduction to computer vision-Writing code to load training data-Coding a Computer Vision Neural Network-Walk through a Notebook for computer vision-Using Callbacks to.

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Exploring a Larger Dataset:-Training with the cats vs. dogs dataset-Working through the notebook-Fixing through cropping-Visualizing the effect of the convolutions-Looking at accuracy and lossPractice Work:-Before you Begin: TensorFlow 2.0 and this Course-The cats vs dogs datasetAugmentation: A technique to avoid overfitting:-Introducing.

Bachelor Of Science - Bs, Computer Science

11-Th Class, General Secondary School, 5.00 Out Of 5.00

Fritl, Programming And Training For Olympiads (C++)

Activities and Societies: ProgrammingProgramming with C++