Top 10 Python Deep Learning Projects
What is Deep Learning?
Deep Learning is an intensive approach. It is a machine learning technique that teaches computer to do what comes naturally to humans. A computer learns to perform classification tasks directly from images, text, or sound.
The term Deep Learning was introduced to artificial neural networks by Igor Aizenberg in 2000. But this actually became popular in 2012 with the victory of ImageNet Competition where winners of this contest actually used Deep learning techniques for Optimizing the solution for Object Recognition.
In this tutorial we are going to see, Top 10 deep learning projects.
Let’s get started
1) Breast Cancer Classification
As we all know cancer is a dangerous disease and it must be detected as soon as possible. It is possible to detect cancer using histopathology images. As cancer cells are different from the regular cells.
What is keras?
Keras is an open-source neural-network library written in Python. It is a high-level API and can run on top of TensorFlow, CNTK, and Theano. Keras is all about enabling fast experimentation and prototyping while running seamlessly on CPU and GPU. It is user-friendly, modular, and extensible.
In this classification our objective is to build a breast cancer classifier on an IDC dataset that can accurately classify a histology image as benign or malignant.
On Kaggle there are many datasets are available related to breast cancer. You can download it from there.
2) Handwritten Digit Recognition
The handwritten digit recognition is the ability of computers to recognize human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different flavours. The handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image.
You should have basic knowledge of python programming, deep learning with keras library and the Tkinter library for building GUI.
Install the necessary libraries for this project.
pip install numpy, keras, tenserflow, pillow
CNN: the input image is fed into the CNN layers. These layers are trained to extract relevant features from the image. Each layer consists of three operation. First, the convolution operation, which applies a filter kernel of size 5×5 in the first two layers and 3×3 in the last three layers to the input. Then, the non-linear RELU function is applied. Finally, a pooling layer summarizes image regions and outputs a downsized version of the input. While the image height is downsized by 2 in each layer, feature maps (channels) are added, so that the output feature map (or sequence) has a size of 32×256.
RNN: the feature sequence contains 256 features per time-step, the RNN propagates relevant information through this sequence. The popular Long Short-Term Memory (LSTM) implementation of RNNs is used, as it is able to propagate information through longer distances and provides more robust training-characteristics than vanilla RNN. The RNN output sequence is mapped to a matrix of size 32×80. The IAM dataset consists of 79 different characters, further one additional character is needed for the CTC operation (CTC blank label), therefore there are 80 entries for each of the 32 time-steps.
CTC: while training the NN, the CTC is given the RNN output matrix and the ground truth text and it computes the loss value. While inferring, the CTC is only given the matrix and it decodes it into the final text. Both the ground truth text and the recognized text can be at most 32 characters long.
On Kaggle there are many datasets are available related to Handwritten Digit Recognition. You can download it from there.
3) Gender and Age Detection Python Project
Age and gender, two of the key facial attributes, play a very foundational role in social interactions, making age and gender estimation from a single face image an important task in intelligent applications, such as access control, human-computer interaction, law enforcement, marketing intelligence and visual surveillance, etc.
Gender Recognition with CNN
Gender recognition using OpenCV’s fisher faces implementation is quite popular and some of you may have tried or read about it also. You can use the OpenCV’s dnn package which stands for Deep Neural
In the dnn package, OpenCV has provided a class called Net which can be used to populate a neural network. Furthermore, these packages support importing neural network models from well-known deep learning frameworks like caffe, tensor flow and torch.
Age Detection with CNN
The CNN’s output layer (probability layer) in CNN consists of 8 values for 8 age classes (“0–2”, “4–6”, “8–13”, “15–20”, “25–32”, “38–43”, “48–53” and “60-”)
On Kaggle there are many datasets are available related to Gender and Age detection. You can download it from there.
4) Traffic Signs Recognition
There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to.
In this Python project example, we have to build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.
This project requires prior knowledge of Keras, Matplotlib, Scikit-learn, Pandas, PIL and image classification.
To install the necessary packages used for this Python data science project, enter the below command in your terminal:
pip install tensorflow keras sklearn matplotlib pandas pil
On Kaggle there are many datasets are available related to Traffic Signs Recognition. You can download it from there.
5) Fake News Detection
Fake news can be come in many forms, including: unintentional errors committed by news aggregators, outright false stories, or the stories which are developed to mislead and influence reader’s opinion. While fake news may have multiple forms, the effect that it can have on people, government and organizations may generally be negative since it differs from the facts.
Our objective is to build a model to accurately classify a piece of news as REAL or FAKE.
The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.
TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms.
IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus.
Passive Aggressive algorithms are online learning algorithms. Such an algorithm remains passive for a correct classification outcome, and turns aggressive in the event of a miscalculation, updating and adjusting. Unlike most other algorithms, it does not converge. Its purpose is to make updates that correct the loss, causing very little change in the norm of the weight vector.
On Kaggle there are many datasets are available related to Fake News Detection. You can download it from there.
6) Music Genre Classification
Machine Learning techniques have proved to be quite successful in extracting trends and patterns from the large pool of data. The same principles are applied in Music Analysis also.
For this project we need a dataset of audio tracks having similar size and similar frequency range. GTZAN genre classification dataset is the most recommended dataset for the music genre classification project and it was collected for this task only.
These sounds are available in many formats which makes it possible for the computer to read and analyse them. Some examples are:
- mp3 format
- WMA (Windows Media Audio) format
- wav (Waveform Audio File) format
Python has some great libraries for audio processing like Librosa and PyAudio.
pip install librosa
Music Genre Classification Approach:
There are various methods to perform classification on this dataset. Some of these approaches are:
- Multiclass support vector machines
- K-means clustering
- K-nearest neighbors
- Convolutional neural networks
On Kaggle there are many datasets are available related to Music Genre Classification. You can download it from there.
7) Colour Detection
Colour detection is the process of detecting the name of any colour. For humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into colour. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the colour. Since childhood, we have mapped certain lights with their colour names. We will be using the somewhat same strategy to detect colour names.
In this colour detection Python project, you have to build an application through which you can automatically get the name of the colour by clicking on them. For this, you will have a data file that contains the colour name and its values. Then you have to calculate the distance from each colour and find the shortest one.
Before starting with this Python project, you should be familiar with the computer vision library of Python that is OpenCV and pandas
On Kaggle there are many datasets are available related to Colour Detection. You can download it from there.
8) Image Caption Generator
You saw an image and your brain can easily tell what the image is about, but can a computer tell what the image is representing? With the advancement in Deep learning techniques, availability of huge datasets and computer power, we can build models that can generate captions for an image.
This is what you can implement in this Python based project where you have to use deep learning techniques of Convolutional Neural Networks and a type of Recurrent Neural Network (LSTM) together.
What is CNN?
Convolutional Neural networks are specialized deep neural networks which can process the data that has input shape like a 2D matrix. Images are easily represented as a 2D matrix and CNN is very useful in working with images.
CNN is basically used for image classifications and identifying if an image is a bird, a plane or Superman, etc.
The objective of this project is to learn the concepts of a CNN and LSTM model and build a working model of Image caption generator by implementing CNN with LSTM.
What is LSTM?
LSTM stands for Long short-term memory, they are a type of RNN (recurrent neural network) which is well suited for sequence prediction problems. Based on the previous text, we can predict what the next word will be. It has proven itself effective from the traditional RNN by overcoming the limitations of RNN which had short term memory. LSTM can carry out relevant information throughout the processing of inputs and with a forget gate, it discards non-relevant information.
This project requires good knowledge of Deep learning, Python, working on Jupyter notebooks, Keras library, Numpy, and Natural language processing.
On Kaggle there are many datasets are available related to Image Caption Generator. You can download it from there.
9) Speech Emotion Recognition
Speech emotion recognition, the best ever python mini project. The best example of it can be seen at call centers. If you ever noticed, call centers employees never talk in the same manner, their way of pitching/talking to the customers changes with customers. Now, this does happen with common people too, but how is this relevant to call centers? Here is your answer, the employees recognize customers’ emotions from speech, so they can improve their service and convert more people. In this way, they are using speech emotion recognition.
What is Speech Emotion Recognition?
Speech Emotion Recognition, abbreviated as SER, is the act of attempting to recognize human emotion and affective states from speech. This is capitalizing on the fact that voice often reflects underlying emotion through tone and pitch. This is also the phenomenon that animals like dogs and horses employ to be able to understand human emotion. SER is tough because emotions are subjective and annotating audio is challenging.
What is Librosa?
librosa is a python library for analysing audio and music. It has a flatter package layout, standardizes interfaces and names, backwards compatibility, modular functions, and readable code.
Our objective is to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset.
pip install librosa soundfile numpy sklearn pyaudio
On Kaggle there are many datasets are available related to Speech Emotion Recognition. You can download it from there.
10) Driver Drowsiness Detection System
The majority of accidents happen due to the drowsiness of the driver. So, to prevent these accidents we can make a system using Python, OpenCV, and Keras which will alert the driver when he feels sleepy.
In this Python project, you will use OpenCV for gathering the images from webcam and feed them into a Deep Learning model which will classify whether the person’s eyes are Open or Closed.
The requirement for this Python project is a webcam through which we will capture images. You need to have Python (3.6 version recommended) installed on your system, then using pip, you can install the necessary packages.
- OpenCV – pip install opencv-python (face and eye detection).
- TensorFlow – pip install tensorflow (keras uses TensorFlow as backend).
- Keras – pip install keras (to build our classification model).
- Pygame – pip install pygame (to play alarm sound).
On Kaggle there are many datasets are available related to Driver Drowsiness Detection. You can download it from there.
These are some deep learning projects you can work on.