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CoderzColumn Tutorials

Learning is a lifelong process. But you must know what, where, and how to learn? What skills to develop? What skills will help you boost your career? If not, you are at the right place! Our tutorial section at CoderzColumn is dedicated to providing you with all the practical lessons. It will give you the experience to learn Python for different purposes and code on your own. Our tutorials cover:

  • Python Programming - threading, multiprocessing, concurrent.futures, asyncio, queue, imaplib, smtplib, email, mimetypes, cprofile, profile, tracemalloc, logging, ipywidgets, beautifulsoup, filecmp, glob, shutil, tarfile, zipfile, argparse, datetime, traceback, abc, contextlib, warnings, dataclasses, re, difflib, textwrap, collections, heapq, bisect, weakref, configparser, signa, ipaddress, xarray, pandas, subprocess, sched, etc.
  • Artificial Intelligence - PyTorch, Keras, Tensorflow, JAX, MXNet, Torchvision, Torchtext, GluonCV, GluonNLP, etc
  • Machine Learning - Scikit-Learn, Statsmodels, XGBoost, CatBoost, LightGBM, optuna, scikit-optimize, hyperopt, bayes_opt, scikit-plot, lime, shap, eli5, etc.
  • Data Science - missingno, seaborn, pandas, sweetviz, numpy, networkx, xarray, awkward-array, etc.
  • Data Visualization - Matplotlib, Bokeh, Bqplot, Plotnine, Altair, Plotly, Cufflinks, Holoviews, dash, streamlit, panel, voila, bokeh, geopandas, geoviews, folium, ipyleaflet, geoplot, cartopy, etc.
  • Digital Marketing - SEO tactics, marketing strategies, Social Media marketing, and more.

For an in-depth understanding of the above concepts, check out the sections below.

Recent Tutorials

Tags keras, LSTM, text-generation, embeddings
Keras: RNNs (LSTM) for Text Generation (Character Embeddings)
Artificial Intelligence

Keras: RNNs (LSTM) for Text Generation (Character Embeddings)

The tutorial explains how to design RNNs (LSTM Networks) for Text Generation Tasks using Python deep learning library Keras. The character embeddings approach is used to encode text data. It uses a character-based approach for text generation.

Sunny Solanki  Sunny Solanki
Jun 07, 2022 ~30 mins
Tags keras, LSTM, text-generation
Keras: Text Generation using LSTM Networks (Character-based RNN)
Artificial Intelligence

Keras: Text Generation using LSTM Networks (Character-based RNN)

The tutorial explains how to create RNNs (LSTM Networks) using Python deep learning library Keras for Text Generation tasks. It uses a character-based approach for text generation.

Sunny Solanki  Sunny Solanki
Jun 05, 2022 ~30 mins
Tags Pytorch, LSTM, text-generation, embeddings
Text Generation using PyTorch LSTM Networks (Character Embeddings)
Artificial Intelligence

Text Generation using PyTorch LSTM Networks (Character Embeddings)

The tutorial explains how to create LSTM Networks (Type of RNN) for Text Generation tasks in Python using Pytorch. The text data is encoded using the character embeddings approach. It uses a character-based approach for text generation.

Sunny Solanki  Sunny Solanki
Jun 03, 2022 ~30 mins
Tags Pytorch, LSTM, text-generation
PyTorch: Text Generation using LSTM Networks (Character-based RNN)
Artificial Intelligence

PyTorch: Text Generation using LSTM Networks (Character-based RNN)

The tutorial explains how to create Recurrent Neural Networks (RNNs) consisting of LSTM layers for Text Generation tasks in Python using Pytorch. It uses a character-based approach for text generation.

Sunny Solanki  Sunny Solanki
Jun 01, 2022 ~30 mins
Tags captum, pytorch, interpretation, text-classificat…
Captum: Interpret Predictions Of PyTorch Text Classification Network
Artificial Intelligence

Captum: Interpret Predictions Of PyTorch Text Classification Network

The tutorial explains how we can use Captum to explain/interpret predictions made by PyTorch networks for Text Classification tasks. Captum and PyTorch are both Python libraries.

Sunny Solanki  Sunny Solanki
May 23, 2022 ~30 mins
Tags captum, pytorch, interpretation, image-classifica…
Captum: Interpret Predictions Of PyTorch Image Classification Networks
Artificial Intelligence

Captum: Interpret Predictions Of PyTorch Image Classification Networks

The tutorial explains how we can use Captum to explain predictions made by PyTorch (Python Deep Learning Library) Image classification networks. It explains various algorithms available from the library.

Sunny Solanki  Sunny Solanki
May 19, 2022 ~30 mins
Tags captum, interpretation, pytorch-networks
Captum: Interpret Predictions Of PyTorch Networks
Artificial Intelligence

Captum: Interpret Predictions Of PyTorch Networks

The captum is a Python library designed specifically to interpret the predictions made by PyTorch networks. PyTorch is a Python deep learning library. The tutorial explains the usage of various algorithms available from Captum.

Sunny Solanki  Sunny Solanki
May 17, 2022 ~30 mins
Tags flax, jax, glove-embeddings, text-classification
Flax (JAX): GloVe Embeddings for Text Classification
Artificial Intelligence

Flax (JAX): GloVe Embeddings for Text Classification

The tutorial explains how we can use GloVe word embeddings with Flax networks for text classification tasks. Flax is a high-level Python deep learning library built on top of the low-level Python library JAX.

Sunny Solanki  Sunny Solanki
May 15, 2022 ~30 mins
Tags keras, glove-embeddings, text-classification
Keras: GloVe Embeddings for Text Classification Tasks
Artificial Intelligence

Keras: GloVe Embeddings for Text Classification Tasks

The tutorial explains how we can use GloVe word embeddings with Keras text classification networks. Keras is a Python deep learning library.

Sunny Solanki  Sunny Solanki
May 13, 2022 ~30 mins
Tags mxnet, glove-embeddings, text-classification
Guide to Use Glove Embeddings with MXNet Networks (GluonNLP)
Artificial Intelligence

Guide to Use Glove Embeddings with MXNet Networks (GluonNLP)

The tutorial explains how we can use GloVe word embeddings with MXNet (Python deep learning library) Networks for text classification tasks. The GloVe embeddings are available from the MXNet helper package Gluonnlp.

Sunny Solanki  Sunny Solanki
May 11, 2022 ~30 mins

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Parallel Computing using Python

Parallel Computing using Python

Parallel Computing is a type of computation where tasks are assigned to individual processes for completion. These processes can be running on a single computer or cluster of computers. Parallel Computing makes multi-tasking super fast.

Python provides different libraries (joblib, dask, ipyparallel, etc) for performing parallel computing.

joblib

joblib
ipyparallel

ipyparallel
dask.distributed

dask.distributed
dask.delayed

dask.delayed
dask.bag

dask.bag
Concurrent Programming in Python

Concurrent Programming in Python

Concurrent computing is a type of computing where multiple tasks are executed concurrently. Concurrent programming is a type of programming where we divide a big task into small tasks and execute these tasks in parallel. These tasks can be executed in parallel using threads or processes.

Python provides various libraries (threading, multiprocessing, concurrent.futures, asyncio, etc) to create concurrent code.

threading

threading
multiprocessing

multiprocessing
concurrent.futures

concurrent.futures
asyncio

asyncio
asyncio - Synchronization Primitives

asyncio - Synchronization Primitives
queue

queue
Visualize Machine Learning Metrics

Visualize Machine Learning Metrics

Once our Machine Learning model is trained, we need some way to evaluate its performance. We need to know whether our model has generalized or not.

For this, various metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc) are designed over time. These metrics help us understand the performance of our models trained on various tasks like classification, regression, clustering, etc.

Python has various libraries (scikit-learn, scikit-plot, yellowbrick, interpret-ml, interpret-text, etc) to calculate and visualize these metrics.

Scikit-Learn: ML Model Evaluation Metrics

Scikit-Learn: ML Model Evaluation Metrics
scikit-plot

scikit-plot
yellowbrick

yellowbrick
interpret-ml

interpret-ml
interpret-text

interpret-text
Interpret Predictions Of ML Models

Interpret Predictions Of ML Models

After training ML Model, we generally evaluate the performance of model by calculating and visualizing various ML Metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc).

These metrics are normally a good starting point. But in many situations, they don’t give a 100% picture of model performance. E.g., A simple cat vs dog image classifier can be using background pixels to classify images instead of actual object (cat or dog) pixels.

In these situations, our ML metrics will give good results. But we should always be a little skeptical of model performance.

We can dive further deep and try to understand how our model is performing on an individual example by interpreting results. Various algorithms have been developed over time to interpret predictions of ML models and many Python libraries (lime, eli5, treeinterpreter, shap, etc) provide their implementation.

LIME

LIME
Shap Values

Shap Values
eli5

eli5
treeinterpreter

treeinterpreter
dice-ml

dice-ml
Python Data Visualization Libraries

Python Data Visualization Libraries

Data Visualization is a field of graphical representation of information / data. It is one of the most efficient ways of communicating information with users as humans are quite good at capturing patterns in data.

Python has a bunch of libraries that can help us create data visualizations. Some of these libraries (matplotlib, seaborn, plotnine, etc) generate static charts whereas others (bokeh, plotly, bqplot, altair, holoviews, cufflinks, hvplot, etc) generate interactive charts. Majority of basic visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are supported by all libraries. Many libraries also support advanced visualization, widgets, and dashboards.

matplotlib (Static Charts)

matplotlib (Static Charts)
bokeh (Interactive Charts)

bokeh (Interactive Charts)
bqplot (Interactive Charts)

bqplot (Interactive Charts)
plotnine (Static Charts)

plotnine (Static Charts)
altair (Interactive Charts)

altair (Interactive Charts)
seaborn (Static Charts)

seaborn (Static Charts)
holoviews (Static & Interactive Charts)

holoviews (Static & Interactive Charts)
cufflinks (Interactive Charts)

cufflinks (Interactive Charts)
hvplot (Interactive Charts)

hvplot (Interactive Charts)
Pandas-Bokeh (Interactive Charts)

Pandas-Bokeh (Interactive Charts)
Advanced Data Visualizations using Python

Advanced Data Visualizations using Python

Basic Data Visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are quite good at representing information and exploring relationships between data variables.

But sometimes these visualizations are not enough and we need to analyze data from different perspectives. For this purpose, many advanced visualizations are developed over time like Sankey diagrams, candlestick charts, network charts, chord diagrams, sunburst charts, radar charts, parallel coordinates charts, etc. Python has many data visualization libraries that let us create such advanced data visualizations.

Candlestick Charts

Candlestick Charts
Sankey Diagrams

Sankey Diagrams
Sunburst Charts

Sunburst Charts
Chord Diagrams

Chord Diagrams
Network Charts

Network Charts
Parallel Coordinates Charts

Parallel Coordinates Charts
Radar Charts

Radar Charts
Treemap Charts

Treemap Charts
Connection Maps

Connection Maps
Choropleth Maps

Choropleth Maps
Scatter & Bubble Maps

Scatter & Bubble Maps
Python Deep Learning Libraries

Python Deep Learning Libraries

Deep learning is a field in Machine Learning that uses deep neural networks to solve tasks. The neural networks with generally more than one hidden layer are referred to as deep neural networks.

Many real-world tasks like object detection, image classification, image segmentation, etc can not be solved with simple machine learning models (decision trees, random forest, logistic regression, etc). Research has shown that neural networks with many layers are quite good at solving these kinds of tasks involving unstructured data (Image, text, audio, video, etc). Deep neural networks nowadays can have different kinds of layers like convolution, recurrent, etc apart from dense layers.

Python has many famous deep learning libraries (PyTorch, Keras, JAX, Flax, MXNet, Tensorflow, Sonnet, Haiku, PyTorch Lightning, Scikeras, Skorch, etc) that let us create deep neural networks to solve complicated tasks.

PyTorch

PyTorch
Keras

Keras
JAX

JAX
Flax

Flax
MXNet

MXNet
PyTorch Lightning

PyTorch Lightning
Haiku

Haiku
Sonnet

Sonnet
Scikeras

Scikeras
Skorch

Skorch
Image Classification

Image Classification

Image classification is a sub-field under computer vision and image processing that identifies an object present in an image and assigns a label to an image based on it. Image classification generally works on an image with a single object present in it.

Over the years, many deep neural networks (VGG, ResNet, AlexNet, MobileNet, etc) were developed that solved image classification task with quite a high accuracy. Due to the high accuracy of these algorithms, many Python deep learning libraries started providing these neural networks. We can simply load these networks with weights and make predictions using them.

Python libraries PyTorch and MXNet have helper modules named 'torchvision' and 'gluoncv’ respectively that provide an implementation of image classification networks.

PyTorch (Torchvision): Image Classification using Pre-trained Networks

PyTorch (Torchvision): Image Classification using Pre-trained Networks
MXNet (GluonCV): Image Classification using Pre-trained Networks

MXNet (GluonCV): Image Classification using Pre-trained Networks
Image Classification using PyTorch CNNs

Image Classification using PyTorch CNNs
Image Classification using Flax (JAX) CNNs

Image Classification using Flax (JAX) CNNs
Image Classification using MXNet CNNs

Image Classification using MXNet CNNs