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Introduction to Deep Neural Networks with Keras and Tensorflow
| Date | Stars |
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| 2026-07-24 | 2967 |
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<div>
<h1 style="text-align: center;">Deep Learning with Keras and Tensorflow</h1>
<img style="text-align: left" src="https://blog.keras.io/img/keras-tensorflow-logo.jpg" width="15%" />
<div>
<br>
### Author: Valerio Maggio
#### Contacts:
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<td style="border: 0px;">
<img src="imgs/twitter_small.png" style="display: inline-block;" />
<a href="http://twitter.com/leriomaggio" target="_blank">@leriomaggio</a>
</td>
<td style="border: 0px;">
<img src="imgs/linkedin_small.png" style="display: inline-block;" />
<a href="it.linkedin.com/in/valeriomaggio" target="_blank">valeriomaggio</a>
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<img src="imgs/gmail_small.png" style="display: inline-block;" />
valeriomaggio_at_gmail
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```shell
git clone https://github.com/leriomaggio/deep-learning-keras-tensorflow.git
```
---
## Table of Contents
- **Part I**: **Introduction**
- Intro to Artificial Neural Networks
- Perceptron and MLP
- naive pure-Python implementation
- fast forward, sgd, backprop
- Introduction to Deep Learning Frameworks
- Intro to Theano
- Intro to Tensorflow
- Intro to Keras
- Overview and main features
- Overview of the `core` layers
- Multi-Layer Perceptron and Fully Connected
- Examples with `keras.models.Sequential` and `Dense`
- Keras Backend
- **Part II**: **Supervised Learning**
- Fully Connected Networks and Embeddings
- Intro to MNIST Dataset
- Hidden Leayer Representation and Embeddings
- Convolutional Neural Networks
- meaning of convolutional filters
- examples from ImageNet
- Visualising ConvNets
- Advanced CNN
- Dropout
- MaxPooling
- Batch Normalisation
- HandsOn: MNIST Dataset
- FC and MNIST
- CNN and MNIST
- Deep Convolutional Neural Networks with Keras (ref: `keras.applications`)
- VGG16
- VGG19
- ResNet50
- Transfer Learning and FineTuning
- Hyperparameters Optimisation
- **Part III**: **Unsupervised Learning**
- AutoEncoders and Embeddings
- AutoEncoders and MNIST
- word2vec and doc2vec (gensim) with `keras.datasets`
- word2vec and CNN
- **Part IV**: **Recurrent Neural Networks**
- Recurrent Neural Network in Keras
- `SimpleRNN`, `LSTM`, `GRU`
- LSTM for Sentence Generation
- **PartV**: **Additional Materials**:
- Custom Layers in Keras
- Multi modal Network Topologies with Keras
---
# Requirements
This tutorial requires the following packages:
- Python version 3.5
- Python 3.4 should be fine as well
- likely Python 2.7 would be also fine, but *who knows*? :P
- `numpy` version 1.10 or later: http://www.numpy.org/
- `scipy` version 0.16 or later: http://www.scipy.org/
- `matplotlib` version 1.4 or later: http://matplotlib.org/
- `pandas` version 0.16 or later: http://pandas.pydata.org
- `scikit-learn` version 0.15 or later: http://scikit-learn.org
- `keras` version 2.0 or later: http://keras.io
- `tensorflow` version 1.0 or later: https://www.tensorflow.org
- `ipython`/`jupyter` version 4.0 or later, with notebook support
(Optional but recommended):
- `pyyaml`
- `hdf5` and `h5py` (required if you use model saving/loading functions in keras)
- **NVIDIA cuDNN** if you have NVIDIA GPUs on your machines.
[https://developer.nvidia.com/rdp/cudnn-download]()
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