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Interactive Course: Introduction to Deep Learning with Keras

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Interactive Course: Introduction to Deep Learning with Keras

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1. In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

2. In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

3. By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

4. It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

1. In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

2. By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

3. In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

4. It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

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Lloyd's Banking Group

"DataCamp is the top resource I recommend for learning data science."

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Harvard Business School

"DataCamp is by far my favorite website to learn from."

Ronald Bowers

Decision Science Analytics @ USAA

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Introduction to Deep Learning with Keras

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In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

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Interactice Course: Introduction to Deep Learning with PyTorch

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Interactice Course: Introduction to Deep Learning with PyTorch

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Loved by learners at thousands of top companies:

Course Description

Neural networks have been at the forefront of Artificial Intelligence research during the last few years, and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being at the same time both powerful and easy to use. In this course you will use PyTorch to first learn about the basic concepts of neural networks, before building your first neural network to predict digits from MNIST dataset. You will then learn about convolutional neural networks, and use them to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. Following the course, you will be able to delve deeper into neural networks and start your career in this fascinating field.

1. In this first chapter, we introduce basic concepts of neural networks and deep learning using PyTorch library.

2. In this third chapter, we introduce convolutional neural networks, learning how to train them and how to use them to make predictions.

3. In this second chapter, we delve deeper into Artificial Neural Networks, learning how to train them with real datasets.

4. In this last chapter, we learn how to make neural networks work well in practice, using concepts like regularization, batch-normalization and transfer learning.

1. In this first chapter, we introduce basic concepts of neural networks and deep learning using PyTorch library.

2. In this second chapter, we delve deeper into Artificial Neural Networks, learning how to train them with real datasets.

3. In this third chapter, we introduce convolutional neural networks, learning how to train them and how to use them to make predictions.

4. In this last chapter, we learn how to make neural networks work well in practice, using concepts like regularization, batch-normalization and transfer learning.

What do other learners have to say?

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Introduction to Deep Learning with PyTorch

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Neural networks have been at the forefront of Artificial Intelligence research during the last few years, and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being at the same time both powerful and easy to use.

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We know that machine learning is the rage these days. But the machine learning technique that shines the most brightly is deep learning. Deep learning is all about how a computer program can learn through observation and make decisions based on its experience. Deep learning methods are useful for computer vision, natural language processing, speech recognition and processing, and so much more.

The best way to learn something is with a hands-on approach and, therefore, we bring these amazing project ideas for you to practice and improve your deep learning knowledge and skills. These project ideas are divided according to there difficulty level so that you can easily find a project that interests you and is within your skill level. So let's not waste any more time and jump right into it.
GOOD NEWS!!
Your headache for finding some really amazing project ideas is finally over. All you need to do is just bookmark this article and you'll never find yourself short of great project ideas to work upon. Here, we have compiled a list of over 500+ project ideas customized specially for you.

1. Python Project Ideas

2. Python Django (Web Development) Project Ideas

3. Python Game Development Project Ideas

4. Python Artificial Intelligence Project Ideas

5. Python Machine Learning Project Ideas

6. Python Data Science Project Ideas

7. Python Deep Learning Project Ideas

8. Python Computer Vision Project Ideas

9. Python Internet of Things Project Ideas

Deep Learning Project Ideas for Beginners

1. Predict Next Sequence

Deep Learning Project Idea - To start with deep learning, the very basic project that you can build is to predict the next digit in a sequence. Create a sequence like a list of odd numbers and then build a model and train it to predict the next digit in the sequence. A simple neural network with 2 layers would be sufficient to build the model.

2. Cats vs Dogs

Deep Learning Project Idea - The cats vs dogs is a good project to start as a beginner in deep learning. You can build a model that takes an image as input and determines whether the image contains a picture of a dog or a cat.

Dataset: Cats vs Dogs Dataset

Source Code: Cats vs Dogs Classification Project

3. Build your Own Neural Net from Scratch

Deep Learning Project Idea - Mostly you will be doing the neural network works using a deep learning library or framework. You should definitely build your own neural network library in order to understand how everything works. This project will enhance your skills and understanding of the subject.

4. Image Classification with CIFAR-10 Dataset

Deep Learning Project Idea - The CIFAR-10 dataset is a collection of images of 10 different classes like cars, birds, dogs, horses, ships, trucks, etc. The idea of the project is to build an image classification model that will be able to identify what class the input image belongs to. Image classification is used in many applications and it is a great project to start deep learning.

Dataset: Image Classification with CIFAR Dataset

Source Code: Image Classification Project

5. Human Face Detection

Deep Learning Project Idea - The face detection took a major leap with deep learning techniques. We can build models with high accuracy in detecting the bounding boxes of the human face. This project will get you started with object detection and you will learn how to detect any object in an image.

Source Code: Human Face Detection Project

6. Dog's Breed Identification

Deep Learning Project Idea - How often do you get stuck thinking about the name of a dog's breed? There are many dog breeds and most of them are similar to each other. We can use the dog breeds dataset and build a model that will classify different dog breeds from an image. This project will be useful for a lot of people.

Dataset: Dog's Breed Identification Dataset

Intermediate Deep Learning Project Ideas

1. Traffic Sign Classification

Deep Learning Project Idea - The traffic sign classification project is useful for all autonomous vehicles. Machines are able to identify traffic signs from the image. You can use the GTSRB dataset that contains 43 different traffic sign classes. This is a good project to understand image classification.

Dataset: Traffic Sign Classification Dataset

Source Code: Traffic Sign Classification Project

2. Automatic Music Generation

Deep Learning Project Idea - What if I told you that you can make music automatically. Yes, it is also possible with deep learning however the real challenge is to generate real music that is pleasant to hear. In this project, the midi file's data can be used to build an LSTM model that can generate new music never heard before.

3. Breast Cancer Classification

Deep Learning Project Idea - Cancer is a dangerous disease and it should be detected as soon as possible. It is possible to detect cancer using histopathology images. Cancer cells differ from regular cells so we can build an image classification model to detect if the person has cancer or not.

Dataset: Breast Cancer Classification Dataset

Source Code: Breast Cancer Classification Project

4. Music Genre Classification

Deep Learning Project Idea - A good project idea is to build a model that can classify the genre of music using neural networks. We would need to extract information from the audio samples such as spectrograms, MFCC, etc. and then use a model to classify the music genre. This model can be used to automatically classify the music genre.

Dataset: Music Genre Classification Dataset
Source Code: Music Genre Classification Project
5. Text Summarizer

Deep Learning Project Idea - The text summarizer is a project in which we make a deep neural network using natural language processing. The idea is to take some paragraphs of text and build their summary. It is very helpful to get a summary of the article.

6. Gender Recognition Using Voice

Deep Learning Project Idea - We can recognize the gender of the person by hearing their voice with good accuracy. Machines can also be trained to recognize different gender voices. We will need labelled audio clips of male and female genders. Then perform feature extraction techniques to feed the data into the classifying model.

7. Chatbot Using Deep Learning
Dataset: Chatbot Using Deep Learning Dataset
Deep Learning Project Idea - Another great project is to make a chatbot using deep learning techniques. Chatbots can be implemented in various ways and a good chatbot also uses deep learning to identify the context the user is asking and then provide it with the relevant answer.

Source Code: Chatbot Using Deep Learning Project

8. Neural Style Transfer

Deep Learning Project Idea - The idea of this project is to make art by using one image and then transferring the style of that image to the target image. This style transfer method is what made the smartphone apps like Prisma famous.

9. Face Aging

Project Idea - With the success of GAN architectures in recent times, we can generate high-resolution modifications to images. You can create an application that takes an input image of a human and returns the pic of the same person of what they'll look in 30 years.

Advanced Deep Learning Project Ideas

1. Gender and Age Detection

Deep Learning Project Idea - You might have seen many smartphone cameras are now equipped with AI. They can even predict if a person is a male or female and their age. This can be done with deep learning but we will need a good amount of data to make this model.

Dataset: Gender and Age Detection Dataset

Source Code: Gender and Age Detection Project
Dataset: Source Code: Driver Drowsiness Detection Project Driver Drowsiness Detection Dataset
2. Driver Drowsiness Detection

Deep Learning Project Idea - The driver drowsiness detection is a project which can detect whether a person is sleeping or not. We can implement a model for drivers and it can also prevent accidents from happening.

3. Human Pose Estimation
Dataset: Human Activity Recognition with Video Classification Dataset
Deep Learning Project Idea - The human pose estimation is the art of identifying body alignment of a person by estimating different body joints. Snapchat uses pose estimation to detect where eyes and head are located to fix a filter on the person. Similarly, we can estimate the human pose and add filters to the person in real-time.

Dataset: Human Pose Estimation Dataset

4. Human Activity Recognition with Video Classification

Deep Learning Project Idea - Build a model that is used to detect human activity like picking something up, putting something down, opening or closing something. Video classification is a difficult task as it requires a series of multiple images to combine together and classify the action that is being performed.

5. Image Caption Generator

Deep Learning Project Idea - Humans can understand an image easily but computers are far behind from humans in understanding the context by seeing an image. However, technology is evolving and various methods have been proposed through which we can automatically generate captions for the image. The project requires knowledge of natural language processing.

Dataset: Image Caption Generator Dataset

Source Code: Image Caption Generator Project

6. Generate Human Faces with DCGAN

Deep Learning Project Idea - DCGAN are Deep Convolutional Generative Adversarial Networks. They are extremely powerful techniques to generate images, audio, text, or videos that are indistinguishable from real-world data. The idea behind this project is to start with random noise and apply DCGAN to generate real-like human faces that don't even exist.

7. Variational Autoencoders

Deep Learning Project Idea - Variational autoencoder or VAE has a huge potential in Deep learning. They can generate new data similar to the data used while training. They have an encoder and a decoder built inside them. For starting you can use the MNIST dataset to generate numbers.

8. Colourizing Old B&W Images

Deep Learning Project Idea - The idea of this project is to make a model that is capable of colorizing old black and white images to colorful images. Digital artists take a few hours to color the image but now with deep learning, it is possible to color an image within seconds.

Source Code: Colorize Black & White Images with Python

9. Language Translator

Deep Learning Project Idea - Human beings take about a year to learn a language but computers can learn in a day. In this project, we can build a language translator app that can translate from English to the French language.

Summary

This was all about DataFlair's Deep Learning project ideas article. In this, we have provided you some best project ideas to work on from basics to advanced level. It is the best way to learn which will help you in enhancing your Deep Learning knowledge and skills.

I hope this article was helpful to you. If you want to add more deep learning project ideas, do let us know in the comment section.

Happy Learning😃

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23 Amazing Deep Learning Project Ideas [Source Code Included] - DataFlair

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We know that machine learning is the rage these days. But the machine learning technique that shines the most brightly is deep learning. Deep learning is all about how a computer program can learn through observation and make decisions based on its experience.

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Introduction

The key to getting better at deep learning (or most fields in life) is practice. Practice on a variety of problems - from image processing to speech recognition. Each of these problem has it's own unique nuance and approach.

But where can you get this data? A lot of research papers you see these days use proprietary datasets that are usually not released to the general public. This becomes a problem, if you want to learn and apply your newly acquired skills.

If you have faced this problem, we have a solution for you. We have curated a list of openly available datasets for your perusal.

In this article, we have listed a collection of high quality datasets that every deep learning enthusiast should work on to apply and improve their skillset. Working on these datasets will make you a better data scientist and the amount of learning you will have will be invaluable in your career. We have also included papers with state-of-the-art (SOTA) results for you to go through and improve your models.

How to use these datasets?

First things first - these datasets are huge in size! So make sure you have a fast internet connection with no / very high limit on the amount of data you can download.

There are numerous ways how you can use these datasets. You can use them to apply various Deep Learning techniques. You can use them to hone your skills, understand how to identify and structure each problem, think of unique use cases and publish your findings for everyone to see!
The datasets are divided into three categories - Image Processing, Natural Language Processing, and Audio/Speech Processing.
Let's dive into it!

Image Datasets

MNIST is one of the most popular deep learning datasets out there. It's a dataset of handwritten digits and contains a training set of 60,000 examples and a test set of 10,000 examples. It's a good database for trying learning techniques and deep recognition patterns on real-world data while spending minimum time and effort in data preprocessing.

Size: ~50 MB

Number of Records: 70,000 images in 10 classes

SOTA: Dynamic Routing Between Capsules

COCO is a large-scale and rich for object detection, segmentation and captioning dataset. It has several features:

Object segmentation

Recognition in context

Superpixel stuff segmentation

330K images (>200K labeled)

1.5 million object instances

80 object categories

91 stuff categories

5 captions per image

250,000 people with keypoints

Size: ~25 GB (Compressed)

Number of Records: 330K images, 80 object categories, 5 captions per image, 250,000 people with key points

Bored with Datasets? Solve real life project on Deep Learning

ImageNet is a dataset of images that are organized according to the WordNet hierarchy. WordNet contains approximately 100,000 phrases and ImageNet has provided around 1000 images on average to illustrate each phrase.

Size: ~150GB

Number of Records: Total number of images: ~1,500,000; each with multiple bounding boxes and respective class labels

SOTA : Aggregated Residual Transformations for Deep Neural Networks

Open Images is a dataset of almost 9 million URLs for images. These images have been annotated with image-level labels bounding boxes spanning thousands of classes. The dataset contains a training set of 9,011,219 images, a validation set of 41,260 images and a test set of 125,436 images.

Size: 500 GB (Compressed)

Number of Records: 9,011,219 images with more than 5k labels

SOTA : Resnet 101 image classification model (trained on V2 data): Model checkpoint, Checkpoint readme, Inference code.

VQA is a dataset containing open-ended questions about images. These questions require an understanding of vision and language. Some of the interesting features of this dataset are:

265,016 images (COCO and abstract scenes)

At least 3 questions (5.4 questions on average) per image

10 ground truth answers per question

3 plausible (but likely incorrect) answers per question

Automatic evaluation metric

Size: 25 GB (Compressed)

Number of Records: 265,016 images, at least 3 questions per image, 10 ground truth answers per question

SOTA : Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

This is a real-world image dataset for developing object detection algorithms. This requires minimum data preprocessing. It is similar to the MNIST dataset mentioned in this list, but has more labelled data (over 600,000 images). The data has been collected from house numbers viewed in Google Street View.

Size: 2.5 GB

Number of Records: 6,30,420 images in 10 classes

SOTA : Distributional Smoothing With Virtual Adversarial Training

This dataset is another one for image classification. It consists of 60,000 images of 10 classes (each class is represented as a row in the above image). In total, there are 50,000 training images and 10,000 test images. The dataset is divided into 6 parts - 5 training batches and 1 test batch. Each batch has 10,000 images.

Size: 170 MB

Number of Records: 60,000 images in 10 classes

SOTA : ShakeDrop regularization

Fashion-MNIST consists of 60,000 training images and 10,000 test images. It is a MNIST-like fashion product database. The developers believe MNIST has been overused so they created this as a direct replacement for that dataset. Each image is in greyscale and associated with a label from 10 classes.

Size: 30 MB

Number of Records: 70,000 images in 10 classes

SOTA : Random Erasing Data Augmentation

Natural Language Processing

This is a dream dataset for movie lovers. It is meant for binary sentiment classification and has far more data than any previous datasets in this field. Apart from the training and test review examples, there is further unlabeled data for use as well. Raw text and preprocessed bag of words formats have also been included.

Size: 80 MB

Number of Records: 25,000 highly polar movie reviews for training, and 25,000 for testing

SOTA : Learning Structured Text Representations

This dataset, as the name suggests, contains information about newsgroups. To curate this dataset, 1000 Usenet articles were taken from 20 different newsgroups. The articles have typical features like subject lines, signatures, and quotes.

Size: 20 MB

Number of Records: 20,000 messages taken from 20 newsgroups

SOTA : Very Deep Convolutional Networks for Text Classification,

Sentiment140 is a dataset that can be used for sentiment analysis. A popular dataset, it is perfect to start off your NLP journey. Emotions have been pre-removed from the data. The final dataset has the below 6 features:

polarity of the tweet

id of the tweet

date of the tweet

the query

username of the tweeter

text of the tweet

Size: 80 MB (Compressed)

Number of Records: 1,60,000 tweets

SOTA : Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets

Mentioned in the ImageNet dataset above, WordNet is a large database of English synsets. Synsets are groups of synonyms that each describe a different concept. WordNet's structure makes it a very useful tool for NLP.

Size: 10 MB

Number of Records: 117,000 synsets is linked to other synsets by means of a small number of "conceptual relations.

SOTA : Wordnets: State of the Art and Perspectives

This is an open dataset released by Yelp for learning purposes. It consists of millions of user reviews, businesses attributes and over 200,000 pictures from multiple metropolitan areas. This is a very commonly used dataset for NLP challenges globally.

Size: 2.66 GB JSON, 2.9 GB SQL and 7.5 GB Photos (all compressed)

Number of Records: 5,200,000 reviews, 174,000 business attributes, 200,000 pictures and 11 metropolitan areas

SOTA : Attentive Convolution

This dataset is a collection of a the full text on Wikipedia. It contains almost 1.9 billion words from more than 4 million articles. What makes this a powerful NLP dataset is that you search by word, phrase or part of a paragraph itself.

Size: 20 MB

Number of Records: 4,400,000 articles containing 1.9 billion words

SOTA : Breaking The Softmax Bottelneck: A High-Rank RNN language Model

This dataset consists of blog posts collected from thousands of bloggers and has been gathered from blogger.com. Each blog is provided as a separate file. Each blog contains a minimum of 200 occurrences of commonly used English words.

Size: 300 MB

Number of Records: 681,288 posts with over 140 million words

SOTA : Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution

This dataset consists of training data for four European languages. The task here is to improve the current translation methods. You can participate in any of the following language pairs:

English-Chinese and Chinese-English

English-Czech and Czech-English

English-Estonian and Estonian-English

English-Finnish and Finnish-English

English-German and German-English

English-Kazakh and Kazakh-English

English-Russian and Russian-English

English-Turkish and Turkish-English

Size: ~15 GB

Number of Records: ~30,000,000 sentences and their translations

SOTA : Attention Is All You Need

Engage with real life projects on Natural Language Processing here

Audio/Speech Datasets

Another entry in this list for inspired by the MNIST dataset! This one was created to solve the task of identifying spoken digits in audio samples. It's an open dataset so the hope is that it will keep growing as people keep contributing more samples. Currently, it contains the below characteristics:

3 speakers

1,500 recordings (50 of each digit per speaker)

English pronunciations

Size: 10 MB

Number of Records: 1,500 audio samples

SOTA : Raw Waveform-based Audio Classification Using Sample-level CNN Architectures

FMA is a dataset for music analysis. The dataset consists of full-length and HQ audio, pre-computed features, and track and user-level metadata. It an an open dataset created for evaluating several tasks in MIR. Below is the list of csv files the dataset has along with what they include:

tracks.csv: per track metadata such as ID, title, artist, genres, tags and play counts, for all 106,574 tracks.

genres.csv: all 163 genre IDs with their name and parent (used to infer the genre hierarchy and top-level genres).

features.csv: common features extracted with librosa.

echonest.csv: audio features provided by Echonest (now Spotify) for a subset of 13,129 tracks.

Size: ~1000 GB

Number of Records: ~100,000 tracks

SOTA : Learning to Recognize Musical Genre from Audio

This dataset contains ballroom dancing audio files. A few characteristic excerpts of many dance styles are provided in real audio format. Below are a few characteristics of the dataset:

Total number of instances: 698

Duration: ~30 s

Total duration: ~20940 s

Size: 14GB (Compressed)

Number of Records: ~700 audio samples

SOTA : A Multi-Model Approach To Beat Tracking Considering Heterogeneous Music Styles

The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are:

To encourage research on algorithms that scale to commercial sizes

To provide a reference dataset for evaluating research

As a shortcut alternative to creating a large dataset with APIs (e.g. The Echo Nest's)

To help new researchers get started in the MIR field

The core of the dataset is the feature analysis and metadata for one million songs . The dataset does not include any audio, only the derived features. The sample audio can be fetched from services like , using provided by Columbia University.

Size: 280 GB

Number of Records: PS - its a million songs!

SOTA : Preliminary Study on a Recommender System for the Million Songs Dataset Challenge

This dataset is a large-scale corpus of around 1000 hours of English speech. The data has been sourced from audiobooks from the LibriVox project. It has been segmented and aligned properly. If you're looking for a starting point, check out already prepared Acoustic models that are trained on this data set at kaldi-asr.org and language models, suitable for evaluation, at http://www.openslr.org/11/.

Size: ~60 GB

Number of Records: 1000 hours of speech

SOTA : Letter-Based Speech Recognition with Gated ConvNets

VoxCeleb is a large-scale speaker identification dataset. It contains around 100,000 utterances by 1,251 celebrities, extracted from YouTube videos. The data is mostly gender balanced (males comprise of 55%). The celebrities span a diverse range of accents, professions and age. There is no overlap between the development and test sets. It's an intriguing use case for isolating and identifying which superstar the voice belongs to.

Size: 150 MB

Number of Records: 100,000 utterances by 1,251 celebrities

SOTA : VoxCeleb: a large-scale speaker identification dataset

Analytics Vidhya Practice Problems

For your practice, we also provide real life problems and datasets to get your hands dirty. In this section, we've listed down the deep learning practice problems on our DataHack platform.

Hate Speech in the form of racism and sexism has become a nuisance on twitter and it is important to segregate these sort of tweets from the rest. In this Practice problem, we provide Twitter data that has both normal and hate tweets. Your task as a Data Scientist is to identify the tweets which are hate tweets and which are not.

Size: 3 MB

Number of Records: 31,962 tweets

This is a fascinating challenge for any deep learning enthusiast. The dataset contains thousands of images of Indian actors and your task is to identify their age. All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup.

Size: 48 MB (Compressed)

Number of Records: 19,906 images in the training set and 6636 in the test set
SOTA: Hands on with Deep Learning - Solution for Age Detection Practice Problem
This dataset consists of more than 8000 sound excerpts of urban sounds from 10 classes. This practice problem is meant to introduce you to audio processing in the usual classification scenario.

Size: Training set - 3 GB (Compressed), Test set - 2 GB (Compressed)

Number of Records: 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes

If you are aware of other open datasets, which you recommend to people starting their journey on deep learning/ unstructured datasets, please feel free to suggest them along with the reasons, why they should be included.

If the reason is good, I'll include them in the list. Let us know your experience with using any of these datasets in the comments section. And happy deep learning!

Not sure, how to start your Deep Learning Journey? Use our Learning Path for Deep Learning

You can also read this article on Analytics Vidhya's Android APP

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25 Open Datasets for Deep Learning Every Data Scientist Must Work With

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Introduction The key to getting better at deep learning (or most fields in life) is practice. Practice on a variety of problems - from image processing to speech recognition. Each of these problem has it's own unique nuance and approach. But where can you get this data?

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Introduction to Deep Learning in Python

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Welcome everyone to an updated deep learning with Python and Tensorflow tutorial mini-series.

Since doing the first deep learning with TensorFlow course a little over 2 years ago, much has changed. It's nowhere near as complicated to get started, nor do you need to know as much to be successful with deep learning.

If you're interested in more of the details with how TensorFlow works, you can still check out the previous tutorials, as they go over the more raw TensorFlow. This is more of a deep learning quick start!

To begin, we need to find some balance between treating neural networks like a total black box, and understanding every single detail with them.

Let's show a typical model:

A basic neural network consists of an input layer, which is just your data, in numerical form. After your input layer, you will have some number of what are called "hidden" layers. A hidden layer is just in between your input and output layers. One hidden layer means you just have a neural network. Two or more hidden layers? Boom, you've got a deep neural network!

Why is this? Well, if you just have a single hidden layer, the model is going to only learn linear relationships.

If you have many hidden layers, you can begin to learn non-linear relationships between your input and output layers.

A single neuron might look as follows:

So this is really where the magic happens. The idea is a single neuron is just sum of all of the inputs x weights, fed through some sort of activation function. The activation function is meant to simulate a neuron firing or not. A simple example would be a stepper function, where, at some point, the threshold is crossed, and the neuron fires a 1, else a 0. Let's say that neuron is in the first hidden layer, and it's going to communicate with the next hidden layer. So it's going to send it's 0 or a 1 signal, multiplied by the weights, to the next neuron, and this is the process for all neurons and all layers.

The mathematical challenge for the artificial neural network is to best optimize thousands or millions or whatever number of weights you have, so that your output layer results in what you were hoping for. Solving for this problem, and building out the layers of our neural network model is exactly what TensorFlow is for. TensorFlow is used for all things "operations on tensors." A tensor in this case is nothing fancy. It's a multi-dimensional array.

To install TensorFlow, simply do a:

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Welcome everyone to an updated deep learning with Python and Tensorflow tutorial mini-series. Since doing the first deep learning with TensorFlow course a little over 2 years ago, much has changed. It's nowhere near as complicated to get started, nor do you need to know as much to be successful with deep learning.