MACHINE LEARNING RECIPES
DATA CLEANING PYTHON
DATA MUNGING
PANDAS CHEATSHEET
ALL TAGS
# What is a relu activation function in keras and why is it used?

# What is a relu activation function in keras and why is it used?

This recipe explains what is a relu activation function in keras and why is it used

Relu activation function in keras and why is it used The Rectified Linear Unit is the most commonly used activation function in deep learning models. The function returns 0 if it receives any negative input, but for any positive value x it returns that value back. So it can be written as y =max(0,x) Some features of Relu function It is very easy to understand, there is no complicated maths formula behind it. It doesn't have the dying slope problem that mainly occurs in other activation functions like sigmoid or tanh. It has some variants in itself for some complicated maths like Leaky Relu and Parametric Relu.

```
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import activations
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from tensorflow.keras import layers
```

We will Define the model and then define the layers, kernel initializer, and its input nodes shape.

```
#Model
model = Sequential()
model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))
```

We will show you how to use ReLU activation functions on some models to works.

```
a = tf.constant([-200, -10, 0.0, 10, 200], dtype = tf.float32)
b= tf.keras.activations.relu(a).numpy()
print(b)
```

[ 0. 0. 0. 10. 200.]

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.

PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

We all at some point in time wished to create our own language as a child! But what if certain words always cooccur with another in a corpus? Thus you can make your own model which will understand which word goes with which one, which words are often coming together etc. This all can be done by building a custom embeddings model which we create in this project

Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

The project will use rasa NLU for the Intent classifier, spacy for entity tagging, and mongo dB as the DB. The project will incorporate slot filling and context management and will be supporting the following intent and entities. Intents : product_info | ask_price|cancel_order Entities : product_name|location|order id The project will demonstrate how to generate data on the fly, annotate using framework and how to process those for different pieces of training as discussed above .

In this deep learning project, you will build a convolutional neural network using MNIST dataset for handwritten digit recognition.

This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

In this machine learning project, you will use the video clip of an IPL match played between CSK and RCB to forecast key performance indicators like the number of appearances of a brand logo, the frames, and the shortest and longest area percentage in the video.