Pinned Repositories
Chicago-crime-seasonal-ARIMA
Forecasting crimes in Chicago city using SARIMA
energy-consumption
Predicting energy consumption using LSTM and combine CNN-LSTM
Natural-Language-Processing-analysis
performing a fully connected convolutional analysis on amazon reveiw
Neural-network-cancer-survival
summarizing the cancer survival dataset, data preparatoins and model configurations.
Scheduling-algo-DQN-AI
The agent’s goal is to maximize the total expected reward over all possible trajectories, even though we defined finite states and action space, there is still a huge number of trajectories, which motivates the use of reinforcement learning [1]. It can be converted as an iterative update in the deep-Q network, which is proposed by Watkins [2] as follows: Q(S_t,A_t )=Q(S_t,A_t )+α[r_(t+1)+γMaxQ(S_(t+1),A_(t+1) )-Q(S_t,A_t )] (2) Where left Q(S_t,A_t ) is the updating Q-values (rewards) at state S_t execute action A_t. r_(t+1)+γMaxQ(S_(t+1),A_(t+1) ) is the predicted target-Q value, where r_(t+1) is the reward when executing action A_(t+1) from state A_t into state A_(t+1).a is learning rate. MaxQ(S_(t+1),A_(t+1) ) is maximum Q-value after executing all possible actions A_(t+1). In DQN, will adopt deep neural network for predicting the Q-values
cotton-leaf-classifier
Cotton leaf Infection Binary Classification Model
preceptron-basic
generating a single layer perceptron
tensorflow-deep-learning
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
a-star-s
Bellabeat-data-analysis
How Can a Wellness Technology Company Play It Smart?
palash-s's Repositories
palash-s/OpenAI
API based OpenAI study material
palash-s/dataset-bi
palash-s/data-
data access for students
palash-s/Scheduling-algo-DQN-AI
The agent’s goal is to maximize the total expected reward over all possible trajectories, even though we defined finite states and action space, there is still a huge number of trajectories, which motivates the use of reinforcement learning [1]. It can be converted as an iterative update in the deep-Q network, which is proposed by Watkins [2] as follows: Q(S_t,A_t )=Q(S_t,A_t )+α[r_(t+1)+γMaxQ(S_(t+1),A_(t+1) )-Q(S_t,A_t )] (2) Where left Q(S_t,A_t ) is the updating Q-values (rewards) at state S_t execute action A_t. r_(t+1)+γMaxQ(S_(t+1),A_(t+1) ) is the predicted target-Q value, where r_(t+1) is the reward when executing action A_(t+1) from state A_t into state A_(t+1).a is learning rate. MaxQ(S_(t+1),A_(t+1) ) is maximum Q-value after executing all possible actions A_(t+1). In DQN, will adopt deep neural network for predicting the Q-values
palash-s/tictactoe
palash-s/a-star-s
palash-s/preceptron-basic
generating a single layer perceptron
palash-s/bitcoin-forecasting
dismantling the bitcoin data
palash-s/Electricity-consumption-in-netherlands
ETA and Time Series analysis on data from one of the power providers in NL
palash-s/fake-news-nlp
Measuring the effect of textVectorization utility on fake News data
palash-s/cotton-leaf-classifier
Cotton leaf Infection Binary Classification Model
palash-s/OOP-python-revision
Contains almost OOP python concepts with examples
palash-s/Natural-Language-Processing-analysis
performing a fully connected convolutional analysis on amazon reveiw
palash-s/certificates
Lists of certificates
palash-s/tensorflow-deep-learning
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
palash-s/Bellabeat-data-analysis
How Can a Wellness Technology Company Play It Smart?
palash-s/Neural-network-cancer-survival
summarizing the cancer survival dataset, data preparatoins and model configurations.
palash-s/energy-consumption
Predicting energy consumption using LSTM and combine CNN-LSTM
palash-s/Chicago-crime-seasonal-ARIMA
Forecasting crimes in Chicago city using SARIMA
palash-s/palash-s.github.io
palash-s/bootstrap
The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.