palash-s

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/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.