TensorFlow & PyTorch Tutorial – Deep Learning Frameworks for Beginners

Learn deep learning frameworks with this beginner-friendly tutorial covering TensorFlow and PyTorch. Understand setup, key features, and best practices to implement neural networks and AI projects efficiently in Python.

1. Introduction

Deep learning frameworks provide tools to build, train, and deploy neural networks efficiently. Two of the most popular frameworks are:

  1. TensorFlow – Developed by Google, widely used for production-ready ML and AI applications.
  2. PyTorch – Developed by Facebook, popular in research and academia for flexibility and dynamic computation.

2. TensorFlow

Overview

  1. Open-source deep learning framework.
  2. Provides APIs for building neural networks, CNNs, RNNs, and more.
  3. Supports deployment on cloud, mobile, and web.

Installation


pip install tensorflow

Basic Example: Simple Neural Network


import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Build model
model = Sequential([Dense(1, input_dim=1)])

# Compile model
model.compile(optimizer='adam', loss='mse')

# Train model
model.fit(X, y, epochs=100)

# Predict
pred = model.predict(np.array([[6]]))
print("Prediction:", pred)

Best Practices:

  1. Use Keras API for easy and readable model building.
  2. Scale data before training.
  3. Monitor training with TensorBoard for visualization.

3. PyTorch

Overview

  1. Open-source deep learning framework.
  2. Provides dynamic computation graphs, useful for experimentation.
  3. Widely used in research and cutting-edge AI projects.

Installation


pip install torch torchvision

Basic Example: Simple Neural Network


import torch
import torch.nn as nn
import torch.optim as optim

# Sample data
X = torch.tensor([[1], [2], [3], [4], [5]], dtype=torch.float32)
y = torch.tensor([[2], [4], [6], [8], [10]], dtype=torch.float32)

# Build model
model = nn.Linear(1, 1)

# Loss and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

# Training
for epoch in range(100):
optimizer.zero_grad()
output = model(X)
loss = criterion(output, y)
loss.backward()
optimizer.step()

# Predict
pred = model(torch.tensor([[6.0]]))
print("Prediction:", pred.item())

Best Practices:

  1. Use DataLoader for batching large datasets.
  2. Take advantage of GPU acceleration with torch.device('cuda').
  3. Debug using small datasets before scaling.

4. Summary

  1. TensorFlow: Production-ready, high-level Keras API, good for deployment.
  2. PyTorch: Flexible and research-friendly, dynamic computation graphs.
  3. Both frameworks enable building ANNs, CNNs, RNNs, and advanced deep learning models efficiently.

Outcome:

  1. Build and train deep learning models using TensorFlow and PyTorch.
  2. Implement CNNs, RNNs, and fully connected networks.
  3. Transition from beginner ML models to advanced AI applications.