https://github.com/mafda/deep_learning_101
Introduction to the basic notions that involve the concept of Machine Learning and Deep Learning. Linear Regression, Logistic Regression, Artificial Neural Networks, Deep Neural Networks, Convolutional Neural Networks.
https://github.com/mafda/deep_learning_101
artificial-neural-networks cnn-keras conda-environment convolutional-neural-networks deep-learning deep-neural-networks keras linear-regression logistic-regression machine-learning mnist neural-network python tensorflow
Last synced: 4 months ago
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Introduction to the basic notions that involve the concept of Machine Learning and Deep Learning. Linear Regression, Logistic Regression, Artificial Neural Networks, Deep Neural Networks, Convolutional Neural Networks.
- Host: GitHub
- URL: https://github.com/mafda/deep_learning_101
- Owner: mafda
- License: mit
- Created: 2018-05-07T17:28:04.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-10-19T03:55:25.000Z (almost 2 years ago)
- Last Synced: 2025-06-03T05:37:24.429Z (4 months ago)
- Topics: artificial-neural-networks, cnn-keras, conda-environment, convolutional-neural-networks, deep-learning, deep-neural-networks, keras, linear-regression, logistic-regression, machine-learning, mnist, neural-network, python, tensorflow
- Language: Jupyter Notebook
- Homepage: https://mafda.medium.com/ml-dl-machine-learning-and-deep-learning-101-2686d93d70d
- Size: 3.65 MB
- Stars: 7
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deep Learning 101
This repository presents the **basic notions** that involve the concept of
Machine Learning and Deep Learning.
Read more in this post [ML & DL — Machine Learning and Deep Learning
101](https://medium.com/@mafda_/ml-dl-machine-learning-and-deep-learning-101-2686d93d70d).
## Configure environment
- Create the conda environment
```shell
(base)$: conda env create -f environment.yml
```
> Mac OS users could use the `environment_ios.yml` file for configuring the iOS
> development environment.
- Activate the environment
```shell
(base)$: conda activate deep_learning_101
```
- Run!
```shell
(deep_learning_101)$: python -m jupyter notebook
```
## Models
The models include a brief theoretical introduction and practical
implementations developed using Python and Keras/TensorFlow in Jupyter
Notebooks.
### Development Environment:
The development environment that will be used as one of the primary frameworks
for machine learning and deep learning, alongside Python programming, is the
Jupyter Notebook environment.
#### 1. Load Data
Load data (training and testing set):
```python
import tensorflow as tf
X_train, y_train = tf.keras.datasets.mnist.load_data()
X_test, y_test = tf.keras.datasets.mnist.load_data()
```
#### 2. Define Model
Two models:
[Sequential](https://keras.io/getting-started/sequential-model-guide/) and
[Functional API](https://keras.io/getting-started/functional-api-guide/).
Sequential used to stack layers:
* `model.add()` used to add the layers.
* `input_shape =()` specify the input form.
```python
model = tf.keras.models.Sequential()
model.add(layer1 …, input_shape=(nFeatures))
model.add(layer2 … )
```
#### 3. Compile Model
Configure the learning process by specifying:
* [`optimizer`](https://keras.io/optimizers) which determines how weights are
updated,
* [Cost function](https://keras.io/losses) or `loss` function,
* [`metrics`](https://keras.io/metrics) to evaluate during training and testing.
```python
model.compile(optimizer='SGD', loss='mse', metrics=['accuracy'])
```
#### 4. Fit Model
Start the training process.
* `batch_size`: divide the data set into a number of batches.
* `epochs`: number of times the data set is trained completely.
```python
model.fit(X_train, y_train, batch_size=500, epochs=1)
```
#### 5. Evaluate Model
Evaluate the performance of the model.
* `model.evaluate()` finds the specified loss and metrics, and it provides a
**quantitative** measure of accuracy.
* `model.predict()` finds the output for the provided test data and it is useful
to check the outputs **qualitatively**.
```python
history = model.evaluate(X_test, y_test)
y_pred = model.predict(X_test)
```
* [ML & DL — Development environment (Part 1)](https://mafda.medium.com/ml-dl-development-environment-part-1-5bb0b35750aa)
### Accuracy results
| Model | Architecture | Activation | Parameters | Accuracy |
| -----: | ------------: | -----------------: | ---------: | -------: |
| LogReg | -- | -- | 7850 | 0.9282 |
| ANN | [32] | [sigmoid] | 25450 | 0.9636 |
| DNN | [128, 64] | [relu, relu] | 25450 | 0.9801 |
| CNN | [32, 64, 128] | [relu, relu, relu] | 25450 | 0.9898 |
### Target - Hypothesis - Cost
| Model | Target | Hypothesis | Cost |
| -----: | ----------------------: | ----------------------------------------------------: | -----------------: |
| LinReg | Continuous | $\hat{y}=Wx+b$ | MSE |
| LogReg | Categorical | $\hat{y}=\sigma(Wx+b)$ | Cross-Entropy |
| ANN | Continuous, Categorical | $\hat{y}=f_{(2)}(f_{(1)}(x))$ | MSE, Cross-entropy |
| DNN | Continuous, Categorical | $\hat{y}=f_{(n)}(...f_{(2)}(f_{(1)}(x)))$ | MSE, Cross-entropy |
| CNN | Continuous, Categorical | $\hat{y}=f_{(n)}(...f_{(2)(\ast)}(f_{(1)(\ast)}(x)))$ | MSE, Cross-entropy |
### Theoretical introduction (https://mafda.medium.com):
* [ML & DL — Linear Regression (Part 2)](https://mafda.medium.com/ml-dl-linear-regression-part-2-14f114f2d62a)
* [ML & DL — Logistic Regression (Part 3)](https://mafda.medium.com/ml-dl-logistic-regression-part-3-fe6aca8f01b)
* [ML & DL — Artificial Neural Networks (Part 4)](https://mafda.medium.com/ml-dl-artificial-neural-networks-part-4-619350a93ef1)
* [ML & DL — Deep Neural Networks (Part 5)](https://mafda.medium.com/ml-dl-deep-artificial-neural-networks-part-5-568ad05be712)
* [ML & DL — Convolutional Neural Networks (Part 6)](https://mafda.medium.com/ml-dl-convolutional-neural-networks-part-6-97357db58165)
### Practical implementations (Jupyter Notebooks):
* [Linear Regression](https://github.com/mafda/deep_learning_101/blob/master/src/01-linear-regression.ipynb)
* [Logistic Regression](https://github.com/mafda/deep_learning_101/blob/master/src/02-logistic-regression.ipynb)
* [Artificial Neural Networks](https://github.com/mafda/deep_learning_101/blob/master/src/03-artificial-neural-networks.ipynb)
* [Deep Neural Networks](https://github.com/mafda/deep_learning_101/blob/master/src/04-deep-neural-networks.ipynb)
* [Convolutional Neural Networks](https://github.com/mafda/deep_learning_101/blob/master/src/05-convolutional-neural-networks.ipynb)
## [pt-BR] Presentation
* [deep-learning-101.pdf](https://github.com/mafda/deep_learning_101/blob/master/pdf/deep-learning-101.pdf)
## References
* Complete Post Medium
* [ML & DL — Machine Learning and Deep Learning 101](https://mafda.medium.com/ml-dl-machine-learning-and-deep-learning-101-2686d93d70d)
* Book
* [Deep Learning Book](http://www.deeplearningbook.org/)
---
made with 💙 by [mafda](https://mafda.github.io/)