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.
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
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
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
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.
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
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’.
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