JAIN NEWS

House Price Prediction using a Random Forest Classifier

  Concerns of Autonomous ARTIFICIAL INTELLIGENCE in Medical Imaging

HUMAN IDENTIFICATION BASED ON IRIS DETECTION TECHNOLOGY

  DRIVERLESS CARS -THE ETHICS OF AUTONOMOUS VEHICLES

  ARTIFICIAL INTELLIGENCE's IMPACT ON FIN-TECH

  Security and Privacy issues with IoT (Internet of Things) - Top barriers to IoT success

  Real World Examples of IoT (Internet of Things) - How will IoT change our lives?

  Internet of Things (IoT) - Next Stage of Information Revolution

  Solving a regression problem using a Sequential Neural Network Model in Keras

  All about Keras Framework in Deep Learning

  Building a CNN model in Keras using MNIST dataset

  Building a simple sequential neural network with dense layers in Keras

  Transfer Learning and Fine Tuning a model in Deep Learning

  Difference between Sigmoid and Softmax function in deep learning

  Regularization Techniques used in Neural Networks in Deep Learning

Hyperparameter Tuning in Neural Networks in Deep Learning In order to minimize the loss and determine optimal values of weight and bias, we need to tune our neural network hyper-parameters. Hyperparameters are the parameters that the neural network can’t learn itself via gradient descent or some other variant.  Hyper-parameters are opposite of learnable parameters. Learnable parameters are automatically learned and then optimized by the neural network. For example, weights and bias are learnable by the neural networks. These are also called trainable parameters as these are optimized during the training process using gradient descent. This is our responsibility to provide optimal values for these hyper-parameters from our experience, domain knowledge and cross-validation. We need to manually tweak these hyperparameters to get better accuracy from the neural network. Following is the list of hyperparameters used in neural networks: 1. Number of hidden layers:  Keep adding the h...

  Global and Local Minima in Gradient Descent in Deep Learning

  What is Dropout? How does it prevent overfitting in a neural network?

  Basic Frameworks to implement Deep Learning Algorithms

  Gradient Descent Algorithm in Deep Learning: Batch, Stochastic and Mini Batch

  Autoencoders in Deep Learning: Components, Types and Applications

  A comparison between Machine Learning and Deep Learning (Machine Learning vs Deep Learning)

  Activation (Squashing) Functions in Deep Learning: Step, Sigmoid, Tanh and ReLu

  Basic introduction of various layers in CNN (Convolutional Neural Network)

  TensorFlow: Tensors, Computational Graphs, Nodes, Estimators and TensorBoard

  Tuples in Python: Indexing, Slicing, Packing, Unpacking, Concatenation, Repetition, Comparison, Membership, Iteration

  Basic introduction of RNN (Recurrent Neural Network) in Deep Learning

  Difference between Decision Tree and Random Forest in Machine Learning

  Advantages and Disadvantages of Linear Regression in Machine Learning

  Data Visualization using Pair Grid and Pair Plot (Seaborn Library)

  Data Visualization using Regression Plot (Seaborn Library)

  Data Visualization using FacetGrid (Seaborn Library)

  Data Visualization using Heatmap (Seaborn Library)

  Data Visualization using Bar Plot (Seaborn Library)

Artificial Intelligence in Sports

  Beautifulsoup: Scraping Google with Python for Searching Job !

Neural Networks (Introduction & Architecture)

  Top Machine Learning Interview Questions

  Correlation vs Covariance

  Outlier Detection

  What are type I and type II errors?

  Linear Regression

  Artificial Neural Networks : Interview Questions

  DIFFERENCE BETWEEN LINEAR REGRESSION AND LOGISTIC REGRESSION

Digit classification using convolutional neural network in keras.

  Text Classification

Types of Chat Bot's

Word Stemming You may have heard me talk about word stemming in the previous tutorial. Stemming a word is attempting to find the root of the word. For example, the word "thats" stem might be "that" and the word "happening" would have the stem of "happen". We will use this process of stemming words to reduce the vocabulary of our model and attempt to find the more general meaning behind sentences. words = [ stemmer . stem ( w . lower ()) for w in words if w != "?" ] words = sorted ( list ( set ( words ))) labels = sorted ( labels ) This code will simply create a unique list of stemmed words to use in the next step of our data preprocessing. Bag of Words Now that we have loaded in our data and created a stemmed vocabulary it's time to talk about a  bag of words . As we know neural networks and machine learning algorithms require numerical input. So out list of strings wont cut it. We need some way to represent our senten...