Python packages for NN&DL Models

-

 Up-to-date (2025) comparison of all major Python packages you can use to build and run Neural Networks & Deep Learning models — ranked by popularity and real-world usage.


Rank

Package

Best For

Difficulty

Speed

Production Ready?

2025 Status & Recommendation

1

TensorFlow + Keras

Everything (beginners → Google-scale production)

Easy → Medium

Very Fast (XLA, GPU/TPU)

Yes (Google, Uber, Airbnb)

#1 Choice in 2025 – Most jobs, best ecosystem, Keras = easiest API

2

PyTorch

Research, flexibility, dynamic graphs

Medium

Very Fast (especially with torch.compile)

Yes (Meta, Tesla, OpenAI)

#2 – Dominant in research & startups

3

JAX + Flax / Equinox

Cutting-edge research, super fast on TPUs

Hard

Fastest on accelerators

Growing (Google DeepMind)

Rising fast in 2025 – used by Grok, AlphaFold

4

Keras (standalone)

Super simple models (now just part of TF)

Very Easy

Fast

Yes

Just use tf.keras – same thing

5

MXNet

Was Amazon’s choice → now abandoned

Medium

Fast

No longer maintained

Dead in 2025 – avoid

6

Sonnet (DeepMind)

Building complex models on JAX/TF

Hard

Fast

Yes (DeepMind only)

Only if you work at DeepMind

7

Haiku

Clean JAX-based models (DeepMind)

Medium-Hard

Fast

Yes

Good but niche

8

LightGBM / XGBoost / CatBoost

Tabular data (not deep learning)

Easy

Very Fast

Yes

Use for structured data – beats NNs

9

Scikit-learn

Simple neural nets (MLP only)

Very Easy

CPU only

Yes

Only for tiny problems

10

Tinygrad

Learning how DL works (minimalist)

Hard

Slow

No

Educational only

Usage of Packages according to your Goal

Your Goal

Use This Package

Learning Deep Learning (beginner)

TensorFlow + Keras

Job interview / most industry jobs

TensorFlow/Keras or PyTorch

Research paper implementation

PyTorch

Fastest training on Google TPU

JAX or TensorFlow

Production at big company

TensorFlow (Google, Airbnb) or PyTorch (Meta, Tesla)

Simple project in 10 lines

Keras (tf.keras)

Tabular data (Kaggle, business)

XGBoost / LightGBM (not DL)

Want to understand from scratch

tinygrad or PyTorch

Comments

Post a Comment

Popular posts from this blog

About me

-
Image
  Associate Professor Anil Neerukonda Institute of Technology & Sciences Sangivalsa, Visakhapatnam, A.P., INDIA. VIDWAN | ORCID | SCOPUS | RESEARCH ID | GOOGLE ID
Read more

A set of documents that need to be classified, use the Naive Bayesian Classifier

-
The Naive Bayes Classifier is a probabilistic machine learning model widely used for classification tasks, including document classification. Based on Bayes' Theorem, it assumes that the features (in this case, words or terms in the documents) are conditionally independent given the class label. Despite this "naive" assumption, it often performs well in practice, especially for text classification. Steps to Perform Document Classification Using Naive Bayes 1.  Prepare the Dataset Documents : Assume you have a set of documents, each labeled with a category (e.g., "Sports", "Politics", "Technology"). Preprocessing : Tokenize the text into words. Remove stop words (e.g., "the", "is", "and"). Perform stemming or lemmatization to reduce words to their base forms. Convert text into a numerical representation, such as a  bag-of-words  or  TF-IDF  vector. 2.  Split the Dataset Divide the dataset into a  training set  and...
Read more

Keras

-
  Understanding Keras Models: Your Neural Network Blueprint Think of a Keras model as the blueprint or architecture for building neural networks. Keras is designed to be simple and lets you create models quickly, while running on powerful engines like TensorFlow in the background. The Three Ways to Build a Keras Model 1. Sequential API: The Straightforward Stack What it is: The easiest way to build a model, like stacking Lego blocks in a single, straight line. Best for: Standard networks where data flows directly from one layer to the next with no fancy detours. Limitation: It's too rigid for complex designs that need multiple inputs, outputs, or layers that share connections. 2. Functional API: The Flexible Designer What it is: A more powerful approach that lets you create complex, branch-like architectures. Best for: Advanced designs with multiple inputs/outputs, skip connections (like shortcuts in the network), or layers that are shared. Example: Building sophisticated models li...
Read more