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Deep learning is a subset of machine learning that uses neural networks to solve complex problems such as image recognition, natural language processing, and more. TensorFlow, Keras, and PyTorch are the most popular frameworks for building deep learning models. This guide will help you get started with each, compare their approaches, and provide practical code examples.
Prerequisites
Before diving in, you should have:
Basic Python programming knowledge
Familiarity with NumPy
Some understanding of linear algebra and matrix operations
Setting Up a Python Virtual Environment
1. Choose a Python Version
Make sure you have the desired Python version installed. You can list installed versions with:
ls /Library/Frameworks/Python.framework/Versions/
2. Create a Virtual Environment
Run the following command to create a virtual environment:
python -m venv myenv
3. This creates a new folder named myenv containing the virtual environment.
4. Activate the Virtual Environment On macOS, run:
source myenv/bin/activate
5. On Windows, run:
myenv\Scripts\activate
6. Your terminal prompt should now show (myenv).
7. Install Required Libraries Use pip to install libraries inside the virtual environment. For example, to install Matplotlib:
pip install matplotlib
8. Deactivate the Environment When finished, deactivate with:
deactivate
TensorFlow
For most recent versions of TensorFlow (2.x), Python 3.8, 3.9, 3.10, or 3.11 are recommended and officially supported. Check the TensorFlow installation guide for the latest compatibility details, as support for newer Python versions may be added over time.
Getting Started with TensorFlow: Code Examples and Explanations
1. Importing TensorFlow
import tensorflow as tf
print(tf.__version__)
Why?
You need to import TensorFlow to access its deep learning tools and check your installed version.
Alternatives:
Keras: import keras (but usually use tf.keras with TensorFlow)
PyTorch: import torch
2. Creating Tensors
import tensorflow as tf
# Create a constant tensor
a = tf.constant([[1, 2], [3, 4]])
print(a)
Why?
Tensors are the basic data structure in TensorFlow, representing multi-dimensional arrays for computation.
Alternatives:
Keras: Uses tensors internally, but you rarely create them directly.
PyTorch: torch.tensor([[1, 2], [3, 4]])
3. Building a Simple Neural Network (Sequential Model)
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Dense(16, activation='relu', input_shape=(4,)),
tf.keras.layers.Dense(3, activation='softmax')
])
Why?
The Sequential model is the simplest way to stack layers for most problems.
How does this help?
It allows you to quickly prototype and build feedforward neural networks.
To use your trained model to make predictions on new data.
Alternatives:
Keras: Same as above. PyTorch: Use model(input_tensor).
Summary Table
Task
TensorFlow / Keras
PyTorch Equivalent
Import
import tensorflow as tf
import torch
Create Tensor
tf.constant()
torch.tensor()
Build Model
tf.keras.Sequential([...])
torch.nn.Sequential([...])
Compile Model
model.compile(...)
Define optimizer/loss manually
Train Model
model.fit(...)
Custom training loop
Evaluate Model
model.evaluate(...)
Custom evaluation loop
Predict
model.predict(...)
model(input)
Keras
Keras is included as part of TensorFlow (tf.keras) and follows TensorFlow’s Python version requirements.
For standalone Keras, Python 3.8, 3.9, 3.10, or 3.11 are generally supported. Always check the Keras release notes or TensorFlow installation guide for the latest compatibility details.
Getting Started with Keras: Code Examples and Explanations
1. Importing Keras
import keras
print(keras.__version__)
Why?
You need to import Keras to access its deep learning tools and check your installed version.
Alternatives:
TensorFlow: import tensorflow as tf (and use tf.keras) PyTorch: import torch
2. Creating Tensors (Standalone Keras uses NumPy arrays)
import numpy as np
# Create a NumPy array (Keras models accept NumPy arrays as input)
a = np.array([[1, 2], [3, 4]])
print(a)
Why?
Keras models use NumPy arrays for input data, which are easy to create and manipulate.
Alternatives:
TensorFlow: tf.constant([[1, 2], [3, 4]])
PyTorch: torch.tensor([[1, 2], [3, 4]])
3. Building a Simple Neural Network (Sequential Model)
from keras.models import Sequential
from keras.layers import Dense
model = Sequential([
Dense(16, activation='relu', input_shape=(4,)),
Dense(3, activation='softmax')
])
Why?
The Sequential model is the simplest way to stack layers for most problems.
How does this help?
It allows you to quickly prototype and build feedforward neural networks.
TensorFlow/Keras: Use model.evaluate(X_train, y_train)
7. Making Predictions
with torch.no_grad():
predictions = model(X_train_tensor[:5])
print(predictions)
Why?
To use your trained model to make predictions on new data.
Alternatives:
TensorFlow/Keras: Use model.predict(X_train[:5])
Summary Table
Task
PyTorch
TensorFlow / tf.keras
Keras (Standalone)
Import
import torch
import tensorflow as tf
import keras
Create Tensor
torch.tensor()
tf.constant()
np.array()
Build Model
nn.Sequential([...])
tf.keras.Sequential([...])
Sequential([...])
Compile Model
Define optimizer/loss manually
model.compile(...)
model.compile(...)
Train Model
Manual training loop
model.fit(...)
model.fit(...)
Evaluate Model
Manual evaluation
model.evaluate(...)
model.evaluate(...)
Predict
model(input)
model.predict(...)
model.predict(...)
Which Deep Learning Library Should You Learn First?
If you are beginning your deep learning journey, you do not need to master all three libraries at once. Here’s a recommended approach:
Start with TensorFlow (using Keras API):
Keras (as tf.keras) is user-friendly and widely used for prototyping and learning deep learning concepts.
TensorFlow’s integration with Keras allows you to build, train, and deploy models easily.
Most tutorials and courses use TensorFlow/Keras, making it a great starting point.
Move to PyTorch:
Once you are comfortable with the basics, learning PyTorch will help you understand deep learning at a lower level.
PyTorch is popular in research and offers more flexibility for custom models and training loops.
Standalone Keras:
Standalone Keras is less commonly used now, as most development has shifted to tf.keras.
You may explore it for legacy projects or to understand its differences, but it’s not essential for most new learners.
In Summary
Begin with TensorFlow/Keras for a gentle introduction and practical experience.
Advance to PyTorch for deeper understanding and flexibility.
Explore standalone Keras only if needed.
Focus on understanding the core concepts (tensors, models, training loops, evaluation, prediction) in one library first—these skills will transfer easily to the others.