# ResNet Implementation

The best way to learn a new framework or a deep learning architecture is to implement it or to try to reproduce it and as a newbie in research like me, a friend suggested that I start with reproducing the ResNet architecture.

This is the 34-layers architecture from the official paper https://arxiv.org/abs/1512.03385

As you can see there’s a beginning layer with 7x7 kernel that’s different from the following layers before I begin explaining the architecture there’s a few thing I would like to clarify for the newbies like myself.

# 647. Palindromic Substrings

Solution explanation

Q: Given a string, your task is to count how many palindromic substrings in this string.

The substrings with different start indexes or end indexes are counted as different substrings even they consist of same characters.

Example 1:

`Input: "abc"Output: 3Explanation: Three palindromic strings: "a", "b", "c".`

Example 2:

`Input: "aaa"Output: 6Explanation: Six palindromic strings: "a", "a", "a", "aa", "aa", "aaa".`

Approach:

Using dynamic programming approach we will form a matrix that consists of indices of two pointers in the given list and the values of say M starting at index 0 and ending…

# Author Note

This is my first attempt to try and summarize a book. I chose “Docker in Action” by Jeff Nickoloff as I found it insightful and interesting especially to those who study software engineering or interested in it. People usually use Docker to containerize their applications. I myself have used Docker in deploying applications. To be frank, I wasn’t familiar with it before; however, thanks to the brilliant blogs and stories from various inspiring people on the internet who seek to share knowledge, I started using it. In the beginning, I didn’t feel that I fully wrapped my mind around the…

# Text classification approaches with code snippets

The data used in this blog post is from Kaggle competition (https://www.kaggle.com/crowdflower/twitter-airline-sentiment#Tweets.csv), you can choose either to download it or to load it through a Kaggle kernel. So let’s start some basic tasks and explore the data

# What we will do

• We will do some data exploration and semantic analysis with a hypothesis and tested it using two different approaches
• We will test different vectorization techniques on different classifiers
• We will Justify our models’ decisions using LIME
• We will use Glove embeddings with fully connected nn, LSTM, Bidirectional LSTM and GRU

## Data Exploration

`df = pd.read_csv('/kaggle/input/twitter-airline-sentiment/Tweets.csv')df.head()`

# Support Vector Machines

Here’s an interesting question: both lines above separate the red and green clusters. Is there a good reason to choose one over another?

Remember that the worth of a classifier is not in how well it separates the training data. We eventually want it to classify yet-unseen data points (known as test data). Given that, we want to choose a line that captures the general pattern in the training data, so there is a good chance it does well on the test data.

The first line above seems a bit “skewed.” Near its lower half it seems to run too…

# The Frequentist vs Bayesian Debate

This is story is the sequel to

# Some statistics

• Random variable (Stochastic variable) — In statistics, the random variable is a variable whose possible values are a result of a random event. Therefore, each possible value of a random variable has some probability attached to it to represent the likelihood of those values.
• Probability distribution — The function that defines the probability of different outcomes/values of a random variable. The continuous probability distributions are described using probability density functions whereas discrete probability distributions can be represented using probability mass functions.
• Conditional probability — This is a measure of probability \$P(A|B)\$ of an…

# Chapter 5: Machine learning basics(part 1)

This story is the summary of my intuition from Deep learning book by Ian Goodfellow, Yoshua Bengio and Aaron Courville

If you haven’t read my previous story on the previous chapters please go check https://medium.com/analytics-vidhya/deep-learning-book-in-plain-english-ch1-2f73e9b71acb

This chapter begins by explaining linear regression, easy ha? I know, but this books really went beyond the simple linear equation we did see in many tutorials so let begin with linear regression equation:

where y-hat is the target value we need to predict, beta-one is the slope or in other words the weight and X is the feature we have from the dataset. The…

# Deep Learning book in plain English Ch1

This story is a summary of my intuition about the Deep learning book (Ch2) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

# Vectors and Tensors

I will start by these two crucial blocks of any system, I am skipping matrices and scalar values as I think they are pretty obvious to many readers.

# Vectors

An array of numbers, we can think of as a set of coordinates having each values in a different axis for example x = [1,2], x is a vector having values in two different axes.

# Tensors

The reason why I began with vectors is actually to compare them with tensors as…

# Deep Learning book in plain English Ch1

This story is a summary of my intuition about the Deep learning book (Ch2) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

## Vectors and Tensors

I will start by these two crucial blocks of any system, I am skipping matrices and scalar values as I think they are pretty obvious to many readers.

## Vectors

An array of numbers, we can think of as a set of coordinates having each values in a different axis for example x = [1,2], x is a vector having values in two different axes.

## Tensors

The reason why I began with vectors is actually to compare them with tensors as…

# Semantic segmentation

What is Semantic Segmentation?
- It the task of assigning a unique label (or category) to every single pixel in the image, which can be considered as a dense classification problem.

Applications for semantic segmentation:

- Autonomous driving
- Medical imaging analysis

# Common deep learning architecture

## AlexNet

The highlights of the architecture

1. Using Relu instead of Tanh; since ReLU is linear (identity) for all positive values, and zero for all negative values. This means that:
• It’s cheap to compute as there is no complicated math. The model can therefore take less time to train or run.
• It converges faster. Linearity means that the slope doesn’t… Data scientist