We know that machine learning is about mapping inputs (such as images) to targets (such as the label “cat”), which is done by observing many examples of input and targets. We also know that deep neural networks do this input-to-target mapping via a deep sequence of simple data transformations (layers) and that these data transformations … Continue reading Understanding how deep learning works: →

"If quantum mechanics hasn't profoundly shocked you, you haven't understood it yet"- Niels Bohr This quote from Niels Bohr seems absolutely correct after observing  the "Double Slit Experiment" Have a look and you will say, "What's the matter with the matter" https://youtu.be/aXvHfCeXd5U

Hi All, I want to discuss Classification report from sklearn in this post. It is important to keep different metrics in mind when we are both training and evaluating models. Depending on the context, certain metrics will make more sense than others. The best thing is that the Classification report summarize it very well in … Continue reading Classification report sklearn →

If you are working with Keras library and want to use tensorboard to print your graphs of accuracy and other variables, Then below are the steps to follow. step 1: Initialize the keras callback library to import tensorboard by using below command from keras.callbacks import TensorBoard step 2: Include the below command in your program … Continue reading Using Tensorboard callback from Keras →

Hello everyone!  Today I want to write about the Pandas library and here are the 30 things you can do with Pandas to better understand the data! First thing first, lets import pandas library: import pandas as pd df=pd.read_csv('test.csv') # read a test file to dataframe (1) Read in a CSV dataset pd.DataFrame.from_csv(“csv_file”) or pd.read_csv(“csv_file”) … Continue reading 30 things you can do with Pandas →

"it’s the coolest idea of Machine Learning in the last 20 years" - Yann LeCun (one of the fathers of Deep Learning) GANs or Generative Adversarial Networks are a kind of neural networks that is composed of 2 separate deep neural networks competing each other: the generator and the discriminator. Their goal is to generate … Continue reading My first Introduction with GAN ( Generative Adversarial Networks ) →

"it’s the coolest idea of Machine Learning in the last 20 years" - Yann LeCun (one of the fathers of Deep Learning) GANs or Generative Adversarial Networks are a kind of neural networks that is composed of 2 separate deep neural networks competing each other: the generator and the discriminator. Their goal is to generate … Continue reading My first Introduction with GAN ( Generative Adversarial Networks) →

Word embeddings are a modern approach for representing text in natural language processing. Embedding algorithms like word2vec and GloVe are key to the state-of-the-art results achieved by neural network models on natural language processing. In this blog, I will show how to train and load word embedding models for natural language processing applications in Python … Continue reading Creating Word Vectors with word2vec and reduce dimensionality using TSNE and Visualizing it using Bokeh →