Top 8 Python Libraries for Deep Learning in 2026

 

1 Tensorflow: The insdustry standard

import tensorflow as tf

x = tf.constant([[1.0, 2.0], [3.0, 4.0]])
y = tf.constant([[5.0, 6.0], [7.0, 8.0]])
result = tf.matmul(x, y)
print(result)

Output:

tf.Tensor(
[[19. 22.]
 [43. 50.]], shape=(2, 2), dtype=float32)

2. Pytocrh- Reasearchers favourite
import torch
x-torch.tensor([[1.,2.],[3.,4.]])
y=torch.tensor([[2.,0.],[0.,2.]])
print(torch.mm(x,y))
Output:
tensor([[2., 4.],
        [6., 8.]])


3. Kera- The beginner deep learning Friend
from tensorflow import keras
from tensorflow.keras import layers
model=keras.Sequential([
    keras.Input(shape=(3,)),
    layers.Dense(4,activation='relu'),
    layers.Dense(1)
])
model.summary()

Output:
Model: "sequential_2"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_5 (Dense)                      │ (None, 4)                   │              16 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_6 (Dense)                      │ (None, 1)                   │               5 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 21 (84.00 B)
 Trainable params: 21 (84.00 B)
 Non-trainable params: 0 (0.00 B)

4. JAX – High-Speed, GPU-Accelerated Computing

import jax.numpy as jnp
from jax import grad
def f(x):
    return x**2+3*x +2

df=grad(f)
print(df(5.0))

Output:
13.0

5. Optuna – Smart Hyperparameter Tuning

import optuna
optuna.logging.set_verbosity(optuna.logging.WARNING)

def objective(trial):
    x = trial.suggest_float('x', -10, 10)
    return (x - 3)**2

study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=10)
print(study.best_params)

Output:
{'x': 1.9064779937795588}

6. Numpy– Foundation of All Deep Learning Math

import numpy as np
x=np.array([[1,2,3],[4,5,6]])

print("Mean:",x.mean())
print("Dot product:\n", np.dot(x,x.T))

Output:
Mean: 3.5
Dot product:
 [[14 32]
 [32 77]]

7. Pandas – Data Handling for Deep Learning

import pandas as pd

data = {'Feature1': [1, 2, 3, 4],
        'Feature2': [2, 3, 4, 5],
        'Target': [3, 5, 7, 9]}

df = pd.DataFrame(data)
print(df.head())
print("Mean of Target:", df['Target'].mean())

Output:
   Feature1  Feature2  Target
0         1         2       3
1         2         3       5
2         3         4       7
3         4         5       9
Mean of Target: 6.0


8. scikit-learn

from sklearn.linear_model import LinearRegression
import numpy as np
X=np.array([[1],[2],[3],[4]])
y=np.array([2,4,6,8])

model=LinearRegression().fit(X,y)
print("Prediction for 5:",model.predict([[5]]))

Output:
Prediction for 5: [10.]