Machine Learning
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Momentum and Adaptive Learning based Gradient Descent Optimizers: Adagrad and Adam
In my previous article on Gradient Descent Optimizers, we had discussed about three types of Gradient Descent algorithms: 1. Batch Gradient Descent...
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Implement a Linear Classification Model using TensorFlow Estimator
Lets see how can we perform linear classification using TensorFlow library in Python. We will use LinearClassifier function from TensorFlow Estimator. We will use...
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Implement a simple Gradient Descent Optimizer in TensorFlow
Lets implement a simple Gradient Descent Optimizer in TensorFlow for a linear model. We will use GradientDescentOptimizer function present in TensorFlow. We will use...
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Constants, Placeholders, Variables and Sessions in TensorFlow
In this article on TensorFlow, we will see how to build and run a graph taking simple examples of constants, placeholders and...
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What is Dropout? How does it prevent overfitting in a neural network?
Dropout is an effective regularization technique used in neural networks which increases generalization capabilities of a deep learning model and prevent it from overfitting. Overfitting in...
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Dying ReLU: Causes and Solutions (Leaky ReLU)
ReLU (Rectified Linear Unit) is a widely used activation function in a neural network which outputs zero if the input is negative...
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Basic Frameworks to implement Deep Learning Algorithms
There are a lot of frameworks available to implement deep learning algorithms. You don’t need to implement CNN, RNN, LSTM algorithms from scratch...
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Gradient Descent Algorithm in Deep Learning: Batch, Stochastic and Mini Batch
Gradient Descent is the widely used algorithm to optimize our neural networks in deep learning. There are a lot of flavors of...
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Autoencoders in Deep Learning: Components, Types and Applications
Autoencoder is a special kind of neural network in which the output is nearly same as that of the input. It is...
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Activation (Squashing) Functions in Deep Learning: Step, Sigmoid, Tanh and ReLu
There are mainly four activation functions (step, sigmoid, tanh and relu) used in neural networks in deep learning. These are also called...
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A comparison between Machine Learning and Deep Learning (Machine Learning vs Deep Learning)
Deep Learning is considered as subset of Machine Learning. Both have a lot of similarities and differences. Lets compare Machine Learning and...
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A journey from a simple Perceptron (Artificial Neuron) to complex Neural Networks
Perceptron is an artificial neuron and is the fundamental unit of a neural network in deep learning. It is also called single...
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Basic introduction of various layers in CNN (Convolutional Neural Network)
CNN (Convolutional Neural Network) is a feed-forward neural network as the information moves from one layer to the next. CNN is also...
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TensorFlow: Tensors, Computational Graphs, Nodes, Estimators and TensorBoard
TensorFlow library was developed by the Google Brain Team for complex numeric calculations (like numpy). It relies on a lot of matrix...
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Basic introduction of RNN (Recurrent Neural Network) in Deep Learning
RNN stands for Recurrent Neural Network. It is a type of neural network which contains memory and best suited for sequential data....
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Online Machine Learning and Deep Learning Quiz (300+ Objective Type Questions)
I have created an online quiz on Machine Learning and Deep Learning. This quiz contains objective type questions on various concepts of...
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Difference between Decision Tree and Random Forest in Machine Learning
Random Forest is a collection of Decision Trees. Decision Tree makes its final decision based on the output of one tree but...
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Advantages and Disadvantages of Linear Regression in Machine Learning
Linear Regression is a supervised machine learning algorithm which is very easy to learn and implement. Following are the advantages and disadvantage...
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Data Exploration and Visualization Techniques in Python
Data Exploration and Visualization is the first step in the process of creating a robust Machine Learning model. We need to understand...
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Data Visualization using Pair Grid and Pair Plot (Seaborn Library)
Lets visualize our data with Pair Grid and Pair Plot which are present in Seaborn library. We can draw various plots (like...
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Data Visualization using Regression Plot (Seaborn Library)
Lets visualize our data with Regression Plot which is present in Seaborn library. By default, Regression Plot uses Scatter Plot. It draws...
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Data Visualization using FacetGrid (Seaborn Library)
Lets visualize our data with Facet Grid which is present in Seaborn library. Facet Grid can be used with Histogram, Scatter Plot, Regression...
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Data Visualization using Violin Plot (Seaborn Library)
Lets visualize our data with Violin Plot which is present in Seaborn library. We can pass various parameters to violinplot like hue, split,...
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Data Visualization using Heatmap (Seaborn Library)
Lets visualize our data with Heatmap which is present in Seaborn library. Heatmap is full of colors. Darker the color, higher is the...
