CoderzColumn : Tutorials Home (Page: 11)

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 optax, learning-rate-schedules
Optax: Learning Rate Schedules for Flax (JAX) Networks
Artificial Intelligence

Optax: Learning Rate Schedules for Flax (JAX) Networks

The tutorial covers learning rate schedules available from Optax Python library which has an implementation of optimizers (SGD, Adam, etc.) used by Flax (JAX) networks. Learning rate schedules anneals learning rate over time during training using various formulas to improve network performance.

Sunny Solanki  Sunny Solanki
Tags learning-rate-schedules, keras
Simple Guide to Learning Rate Schedules for Keras Networks
Artificial Intelligence

Simple Guide to Learning Rate Schedules for Keras Networks

Learning rate schedules anneals learning rate during training over time to improve the performance of networks. The tutorial covers the majority of learning rate schedules available in Python deep learning library keras as well as explains how to create your custom schedule to anneal learning rate.

Sunny Solanki  Sunny Solanki
Tags keras, callbacks
Simple Guide To Keras Callbacks
Artificial Intelligence

Simple Guide To Keras Callbacks

Keras callbacks are Python functions that let us perform some functionalities at the start of training, end of the training, the start of a batch, the end of a batch, the start of epoch, and the end of an epoch. It let us change learning rate, stop training, save model/weights, log metrics, etc. The tutorial explains them with simple examples.

Sunny Solanki  Sunny Solanki
Tags recommendation-systems, collaborative-filtering, …
Recommender Systems (Collaborative Filtering) using Flax (JAX)
Artificial Intelligence

Recommender Systems (Collaborative Filtering) using Flax (JAX)

The tutorial covers a simple guide to designing recommendation systems using a collaborative filtering approach and Flax (JAX) Python deep learning framework. It covers 4 different simple models to solve the recommendation task.

Sunny Solanki  Sunny Solanki
Tags haiku, cnn, jax
Haiku: Convolutional Neural Networks (CNNs)
Artificial Intelligence

Haiku: Convolutional Neural Networks (CNNs)

The tutorial covers how we can create convolutional neural networks using Haiku. It explains the different ways of creating neural nets and trains them with SGD and Adam optimizers. It also covers how to handle channels first vs channels last data formats. We try to solves image classification task using CNNs.

Sunny Solanki  Sunny Solanki
Tags sonnet, tensorflow, cnn
Sonnet: Convolutional Neural Networks (CNNs)
Artificial Intelligence

Sonnet: Convolutional Neural Networks (CNNs)

The tutorial covers how we can create convolution neural networks using Sonnet. It also explains how we can handle channels first vs channels last data format with images. We try to solves image classification task using CNNs.

Sunny Solanki  Sunny Solanki
Tags pytorch-lightning, neural-networks
PyTorch Lightning: Simplify Model Training by Eliminating Loops
Artificial Intelligence

PyTorch Lightning: Simplify Model Training by Eliminating Loops

PyTorch Lightning is a framework designed on the top of PyTorch to simplify the training process performed through loops. The tutorial explains how we can avoid loops for training, validation, and prediction when working with PyTorch using PyTorch Lightning.

Sunny Solanki  Sunny Solanki
Tags flax, cnn
Flax: Convolutional Neural Networks (CNN)
Artificial Intelligence

Flax: Convolutional Neural Networks (CNN)

The tutorial guides individuals to create Convolutional Neural Networks using the Flax framework. Flax is a high-level flexible framework designed on top of JAX to make the task of designing neural networks easier. We try to solves image classification task using CNNs.

Sunny Solanki  Sunny Solanki
Tags mxnet, cnn
MXNet: Convolutional Neural Networks (CNN)
Artificial Intelligence

MXNet: Convolutional Neural Networks (CNN)

The tutorial explains with simple examples how to create Convolutional Neural Networks using the Gluon API of MXNet framework. It also explains how to handle Channels First vs Channels Last formats. We try to solves image classification task using CNNs.

Sunny Solanki  Sunny Solanki
Tags pytorch, cnn
PyTorch - Convolutional Neural Networks
Artificial Intelligence

PyTorch - Convolutional Neural Networks

The tutorial covers a guide to creating a convolutional neural networks using PyTorch. It explains how to create CNNs using high-level PyTorch API available through ‘torch.nn’ Module. We try to solves image classification task using CNNs.

Sunny Solanki  Sunny Solanki
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.

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.

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.

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.

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.

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.

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.

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.