Artificial Intelligence

Convolutional Neural Networks (CNN)

Convolutional Neural Network (CNN) is an neural network which extracts or identifies a feature in a particular image and is the basis of GoogleNet and VGG19 and used for object detection and classification. CNN has five basic components Convolution, ReLU, Pooling, Flattening and Full connection.

Machine Learning (ML)

Deep Learning on 2-Dimensional Images

Applying deep learning concepts on images has proved to be one of the important work which has resulted in early detection of diseases resulting in saving millions of life to monitoring activities on the entire Earth We take a look at medical images, Satellite Images and the various Python libraries

Machine Learning (ML)

Deep Learning for Medical Imaging and Diagnosis

One of the major medical challenges that we face today is the early detection of diseases so that the proper threatment can be applied. This can be solved by applying machine learning to analyse MRI scan, CT Scan, Xray Scans, InfraRed Images, Arthoscopy, UV radiation, Scintigraphy and Ultrasound

Machine Learning (ML)

When to use Multilayer Perceptrons (MLP)?

Multilayer Perceptrons (MLPs) are the buiding blocks of neural network. They are comprised of one or more layers of neurons. MLPs are used for classification prediction problems, regression prediction problems and tabular datasets.

Machine Learning (ML)

Popular Datasets in Machine Learning

Data sets are important in Machine learning as the more better data we have, the better the model. The various popular data sets available for machine learning are ImageNet, MNIST, NIST, CIFAR-10 and YouTube 8M.

Machine Learning (ML)

Basic Linear Algebra Subprograms (BLAS) Library

The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. There are three levels within the BLAS library. The various implementations include Intel's MKL, BLIS, NetLib's BLAS, OpenBLAS, BLAS++ and others

Machine Learning (ML)

Run a ResNet34 model in ONNX format on TVM Stack with LLVM backend

In this guide, we will run a ResNet34 model in ONNX format on the TVM Stack with LLVM backend. You do not need any specialized equipment like GPU and TPU to follow this guide. A simple CPU is enough.

Machine Learning (ML)

Run a ResNet18 model in ONNX format on TVM Stack with LLVM backend

In this guide, we will run a ResNet18 model in ONNX format on the TVM Stack with LLVM backend. You do not need any specialized equipment like GPU and TPU to follow this guide. A simple CPU is enough.

Machine Learning (ML)

Run a ResNet101 model in ONNX format on TVM Stack with LLVM backend

In this guide, we will run a ResNet101 model in ONNX format on the TVM Stack with LLVM backend. You do not need any specialized equipment like GPU and TPU to follow this guide. A simple CPU is enough.

Machine Learning (ML)

Run a ResNet152 model in ONNX format on TVM Stack with LLVM backend

In this guide, we will run a ResNet152 model in ONNX format on the TVM Stack with LLVM backend. You do not need any specialized equipment like GPU and TPU to follow this guide. A simple CPU is enough.

Machine Learning (ML)

Run a ResNet50 model in ONNX format on TVM Stack with LLVM backend

In this guide, we will run a ResNet50 model in ONNX format on the TVM Stack with LLVM backend. You do not need any specialized equipment like GPU and TPU to follow this guide. A simple CPU is enough.

Machine Learning (ML)

Run a VGG16 model in ONNX format on TVM Stack with LLVM backend

In this guide, we will run a VGG16 model in ONNX format on the TVM Stack with LLVM backend. You do not need any specialized equipment like GPU and TPU to follow this guide. A simple CPU is enough.

Machine Learning (ML)

Run a VGG19 model in ONNX format on TVM Stack with LLVM backend

In this guide, we will run a VGG19 model in ONNX format on the TVM Stack with LLVM backend. You do not need any specialized equipment like GPU and TPU to follow this guide. A simple CPU is enough.

Machine Learning (ML)

Install TVM and NNVM from source

In this guide, we will walk you through the process of installing TVM and NNVM compiler from source along with all its dependencies such as HalideIR, DMLC-CORE, DLPACK and COMPILER-RT. Once installed, you can enjoy compiling models in any frameworks on any backend of your choice.

Machine Learning (ML)

TVM: A Deep Learning Compiler Stack

TVM is an open source deep learning compiler stack for CPUs, GPUs, and specialized accelerators that takes in models in various frameworks like TensorFlow, Keras, ONNX and others and deploys them on various backends like LLVM, CUDA, METAL and OpenCL. It gives comparably better performance than other

Artificial Intelligence

L1 and L2 Regularization Methods

A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Lasso shrinks the less important feature’s coefficient to zero

Artificial Intelligence

Gradient descent: Mathematical view

Gradient descent algorithm is one of the most popuarl algorithms for finding optimal parameters for most machine learning models including neural networks. The basic method that this algorithm uses is to find optimal values for the parameters that define your ‘cost function’.

Artificial Intelligence

Regularization

Regularization is a method used to reduce the variance of your model and increase the bias. It is used when your model overfits the training data. Another method to do regularization is called Lasso regression. This is the solution to Biase-Variance Dilemma.

Artificial Intelligence

Cross Validation

Cross Validation is a procedure used to evaluate your machine learning model on limited sample of data. With the help of this, we can actually tell how well our model performs on unseen data. Other variants are stratified cross validation and leave one out cross validation. Learn through an example

Artificial Intelligence

Decision Trees

Decision Tree is a popular machine learning algorithm mainly used for classification. Concepts of entropy and information gain are required to apply decision tree for a data set. It is used for non-linear classification and regression Learn through an example