Python Virtual Environment Setup

How Neural Network Works Information is fed into the input layer which transfers it to the hidden layer The interconnections between the two layers assign weights to each input randomly A bias added to every input after weights are multiplied with them individually The weighted sum is transferred to the activation function The activation…

What is Central Limit Theorem and why it is important? Given a sufficiently large sample size, the sampling distribution of the mean for a variable will approximate a normal distribution regardless of that variable’s distribution in the population. Importance: This theorem allows simplifying problems in statistics by allowing to work with a distribution that is approximately normal. What is sampling? How many sampling methods do you know?

Why should we convert text to a vector? The computer understands the numerical value Once we convert text to vector we can leverage the power of algebra What rules need to be followed while converting text to a vector? In the case of a review, if R1 and R2 are…

If you have a classification problem in hand data is almost linearly separable you can try with Logistic Regression. Wondering we are classifying, but the technique is called Logistic Regression? You might be knowing the equation of a line in 2D is y = mx+c. In Logistic regression, we use…

OK, so before diving into performance metrics let's make sure we understand Type 1 and Type 2 error. Type 1 error is False Positive Type 2 error is False Negative 1. Confusion Metrics Pros Highly interpretable Gives an overview of where the model failing Cons It does not use class probability Since we…

Choosing an initializer is an active area of research and there is no definite rule to choose an initializer. But these are some guidelines which came with experience. Few points which we need to keep in mind while initializing weight. Weight should be small (not too small) Not all Zero…

Here are a few checklists which one can follow while training Deep Learning Models. 1. Preprocess the data. For numeric data, you can scale or normalize. For text data, we can remove stop words, tokenize them, convert them to vector representation using TFIDF, BoW, Word2Vec, etc. 2. Initialize the weights appropriately. There are a few weight initialization techniques. a…

Elements of Deep Neural Network Xi is a four-dimensional feature of a data point. Xij represents each feature of the data point. Y_Pred is a real number. This means here we are solving a regression problem in this example. F is the Activation Function. O is Output. W is the weight. We have weights associated…