📚Chapter 3: Sentiment Analysis (Naive Bayes) This tutorial, I’ll show you how to train the Naive Bayes classifier. In this context, we train in something different than in logistic regression or deep learning. There is no gradient descent. We’re just counting frequencies of words in the corpus. You will now be creating step-by-step, a Naive Bayes…

Description In this tutorial and in the tutorial after this one, I wanna tell you about some of the practical tricks for making gradient descent work well. In this Tutorial, I want to tell you about an idea called feature skill. Here’s the idea. Sections Gradient on original data (without Feature scaling…

Introduction: In the realm of computer vision, where algorithms strive to make sense of the visual world, the presence of noise can be akin to a discordant note in an otherwise harmonious melody. Noise, in the context of computer vision, refers to unwanted or random variations in pixel values that can…

Sections What is Vectorization Python Implementation CPU and GPU Important points Benefits of Vectorization Common Vectorization Techniques Examples of Vectorization Section 1- What is Vectorization ( Dr Andrew) Vectorization is basically the art of getting rid of explicit for loops in your code. In the deep learning era, especially in deep learning in practice, you often find yourself training…

Introduction: Python, a versatile and powerful programming language, offers a wide range of data structures to handle and manipulate data efficiently. One of the fundamental and frequently used data structures in Python is the list. …

Description In the previous Tutorial, we talked about the form of the hypothesis for linear regression with multiple features or with multiple variables. In this Tutorial, let’s talk about how to fit the parameters of that hypothesis. In particular let’s talk about how to use gradient descent for linear regression…

📚Chapter 3: Sentiment Analysis (Naive Bayes) We will continue from the previous Tutorial and show you how to do inference. Given your lambda dictionary the task is pretty straightforward. You have done most of the work to arrive at your log likelihood already. Now let’s wrap up. Now you can calculate the log likelihood of the…

Introduction In the vast realm of computer vision, where algorithms and techniques constantly evolve, one fundamental operation stands out for its simplicity and effectiveness: scalar multiplication. This seemingly basic mathematical operation plays a pivotal role in manipulating and enhancing images, providing a versatile tool for a wide array of applications. Sections Understanding…

Introduction In the vast landscape of data science, one inevitable challenge is dealing with missing data. Missing values can arise from a variety of reasons, ranging from human error during data collection to technical issues in data storage. In this blog post, we’ll explore how scikit-learn, a powerful machine learning library…