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vi docker-compose.yml To get Elasticsearch cluster and kibana up and running in Docker, you can use Docker Compose. If you don’t want to expose port 9200 and instead use a reverse proxy, replace 9200:9200 with 127.0.0.1:9200:9200 in the docker-compose.yml file. Elasticsearch will then only be accessible from the host machine itself(www.elastic.co/guide/en/elasticsearch/reference/current/docker.h..

curl -L "https://github.com/docker/compose/releases/download/1.28.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose Install the docker-compose. # sudo curl -L "https://github.com/docker/compose/releases/download/1.28.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Tota..

Keras 1. Preprocessing from tensorflow.keras.preprocessing.text import Tokenizer t=Tokenizer() fit_text='The earth is an awesome place live' t.fit_on_texts([fit_text]) test_text='The earth is an great place live' sequences=t.texts_to_sequences([test_text])[0] sequences >>>[1, 2, 3, 4, 6, 7] t.word_index >>>{'an': 4, 'awesome': 5, 'earth': 2, 'is': 3, 'live': 7, 'place': 6, 'the': 1} Tokenizer.fi..

Dropout - One way to avoid overfitting, using several neurons only rather than whole neurons when machine training.- It does not have any learnable parameters and has only one hyperparameter.- It does not behave similarly during training and testing.  To be understand this, let us consider a simple fully connected layer containing 10 neurons. We are using a dropout probability of 0.5. Well durin..

Forward Propagation Computation z : variable, accumulated sum from previous layers of x * w, before applied sigmoid(or whatever activation function) function z1=W1x1+W2x2=0.3×0.1+0.25×0.2=0.08z1=W1x1+W2x2=0.3×0.1+0.25×0.2=0.08 z2=W3x1+W4x2=0.4×0.1+0.35×0.2=0.11 Then, z1 and z2 shoulc through sigmoid(or whatever activation function) function in each hidden layer. h1=sigmoid(z1)=0.51998934h1=sigmo..