# How to Find Deep Learning Libraries in Awesome-Python

> Discover deep learning libraries in the awesome-python repository. Find 67 curated frameworks easily under the Machine Learning Deep Learning section of the README.md file. Explore now.

- Repository: [Dylan Hogg/awesome-python](https://github.com/dylanhogg/awesome-python)
- Tags: deep-dive
- Published: 2026-03-01

---

**Deep learning libraries in the awesome-python repository are organized under the "Machine Learning – Deep Learning" section of [`README.md`](https://github.com/dylanhogg/awesome-python/blob/main/README.md), which contains 67 curated frameworks with direct links to their source repositories.**

The awesome-python project by dylanhogg serves as a definitive, community-curated catalog of Python packages. When searching for deep learning libraries in awesome-python, you will find them systematically arranged within a dedicated Machine Learning subsection that includes both established frameworks and emerging specialized tools.

## Locating the Deep Learning Section in README.md

All library listings in awesome-python reside in the main [`README.md`](https://github.com/dylanhogg/awesome-python/blob/main/README.md) file at the repository root. The deep learning collection specifically appears under the **"Machine Learning – Deep Learning"** heading, accessible via the GitHub anchor link `#machine-learning---deep-learning`.

### Direct Link and Navigation

You can jump directly to the section using this URL:

```markdown
https://github.com/dylanhogg/awesome-python/blob/main/README.md#machine-learning---deep-learning

```

Within this section, libraries appear as a numbered list where each entry provides a concise description and a hyperlink to the project's GitHub repository.

## Notable Deep Learning Libraries Listed

The awesome-python deep learning collection currently features **67 libraries** spanning frameworks, optimization tools, and specialized models. The list prioritizes actively maintained projects with substantial community adoption.

### Frameworks and Optimization Tools

The section leads with major industry frameworks followed by performance optimization libraries:

| # | Library | Description | Link |

|---|---------|-------------|------|
| 1 | **TensorFlow** | Open-source Machine Learning framework for everyone | https://github.com/tensorflow/tensorflow |
| 2 | **PyTorch** | Tensors and dynamic neural networks with strong GPU acceleration | https://github.com/pytorch/pytorch |
| 3 | **Whisper** | Robust speech-recognition model trained on large-scale weak supervision | https://github.com/openai/whisper |
| 4 | **Keras** | "Deep Learning for humans" – high-level API running on TensorFlow, JAX, or CNTK | https://github.com/keras-team/keras |
| 5 | **DeepSpeed** | Deep-learning optimization library for efficient distributed training and inference | https://github.com/microsoft/deepspeed |

Additional entries include image-generation models, graph-neural-network libraries, and 3-D vision toolkits.

## Getting Started with Popular Libraries

The following code examples demonstrate basic usage patterns for three prominent libraries featured in the awesome-python list.

### Installing Core Dependencies

Install the primary frameworks using pip:

```bash
pip install tensorflow torch transformers

```

Note that Whisper functions within the broader transformers ecosystem.

### TensorFlow Implementation

Create a simple neural network using TensorFlow's Keras API:

```python
import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy')
print("TensorFlow model summary:")
model.summary()

```

This example constructs a feed-forward network with one hidden layer containing 10 units.

### PyTorch Implementation

Implement a comparable architecture using PyTorch's module system:

```python
import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(4, 10)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(10, 1)
        self.sigmoid = nn.Sigmoid()
    
    def forward(self, x):
        return self.sigmoid(self.fc2(self.relu(self.fc1(x))))

net = SimpleNet()
print("PyTorch model architecture:")
print(net)

```

This defines an equivalent two-layer network with ReLU activation and sigmoid output.

### Whisper for Speech Recognition

Transcribe audio using the Whisper model through the transformers pipeline:

```python
from transformers import pipeline

transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-large")
result = transcriber("path/to/audio.wav")
print("Transcription:", result["text"])

```

This pipeline leverages Whisper's robust speech recognition capabilities trained on large-scale weak supervision.

## Summary

- Deep learning libraries in awesome-python are located in the **"Machine Learning – Deep Learning"** section of [`README.md`](https://github.com/dylanhogg/awesome-python/blob/main/README.md)
- The section contains **67 curated libraries** including TensorFlow, PyTorch, Keras, and DeepSpeed
- Each entry includes a direct GitHub link and concise description for immediate exploration
- The repository provides practical starting points for frameworks ranging from general-purpose to specialized domains like speech recognition and distributed training

## Frequently Asked Questions

### Where exactly are deep learning libraries listed in awesome-python?

Deep learning libraries appear under the **"Machine Learning – Deep Learning"** heading within the main [`README.md`](https://github.com/dylanhogg/awesome-python/blob/main/README.md) file. You can navigate directly to this section using the anchor link `#machine-learning---deep-learning` in the GitHub interface.

### How many deep learning libraries are included in the awesome-python list?

The awesome-python repository currently catalogs **67 deep learning libraries** in this section. This collection spans from major frameworks like TensorFlow and PyTorch to specialized tools for optimization, computer vision, and audio processing.

### What types of deep learning tools are featured beyond frameworks?

Beyond core frameworks, the list includes optimization libraries like **DeepSpeed** for distributed training, speech recognition models like **Whisper**, and specialized packages for image generation, graph neural networks, and 3-D vision applications.

### Can I contribute new deep learning libraries to awesome-python?

While the source analysis focuses on consumption mechanics, awesome-python follows standard GitHub contribution workflows. You would submit a pull request modifying the [`README.md`](https://github.com/dylanhogg/awesome-python/blob/main/README.md) to add new libraries that meet the repository's curation criteria for quality and maintenance.