Differences Between Supervised Learning vs Deep Learning
Differences Between Supervised Learning vs Deep LearningIn supervised learning, the training data you feed to the algorithm includes the desired solutions, called labels. A typical supervised learning task is classification. The spam filter is a good example of this: it is trained with many example emails along with their class (spam or ham), and it must learn how to classify new emails.
Deep learning is an attempt to mimic the activity in layers of neurons in the neocortex, which is about 80% of the brain where thinking occurs(In a human brain, there are around 100 billion neurons and 100 ~ 1000 trillion synapses). It is called deep because it has more than one hidden layers of neurons which help to have multiple states of nonlinear feature transformation
Head To Head Comparison of Supervised Learning vs Deep Learning (Infographics)
Below is The Top 5 Comparison Between Supervised Learning vs Deep Learning
Key Differences between Supervised Learning vs Deep Learning
Both Supervised Learning vs Deep Learning are popular choices in the market; let us discuss some of the major Differences Between Supervised Learning vs Deep Learning:
●Major Models –
Important supervised models are –
○k-Nearest Neighbors – Used for classification and regression
○Linear Regression – For Prediction/regression
○Logistic Regression – For Classification
○Support Vector Machines (SVMs) – Used for classification and regression
○Decision Trees and Random Forests – Both classification and regression tasks
Most popular Deep neural networks :
●Multilayer Perceptrons ( MLP) – Most basic type. This network is generally the starting phase of building other more sophisticated deep network and can be used for any supervised regression or classification problems
●Autoencoders (AE) – Network has unsupervised learning algorithms for feature learning, dimension reduction, and outlier detection
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●Convolution Neural Network (CNN) – particularly suitable for spatial data, object recognition and image analysis using multidimensional neurons structures. One of the main reason for the popularity of the deep learning lately is due to CNN’s.
●Recurrent Neural Network (RNN) – RNNs are used for sequenced data analysis such as time-series, sentiment analysis, NLP, language translation, speech recognition, image captioning. One of the most common types of RNN model is Long Short-Term Memory (LSTM) network.
●Training Data – As mentioned earlier, supervised models needs training data with labels. But Deep learning can handle data with or without labels. Some neural network architectures can be unsupervised, such as autoencoders and restricted Boltzmann machines
●Feature Selection – Some Supervised models are capable of analyzing features and a select subset of feature to determine the target. But most of the time this has to be handled in data preparation phase. But in Deep Neural Networks, new features are emerged and unwanted features are discarded as learning progress.
●Data Representation – In classical supervised models, high-level abstraction of input features are not created. Final model trying to predict output by applying mathematical transforms on a subset of input features.
But in deep neural networks, abstractions of input features are formed internally. For example, while translating text, the neural network first convert input text to internal encoding then transform that abstracted representation to target language.
●Framework -Supervised ML models are supported by a lot of generic ML frameworks across different languages – Apache Mahout, Scikit Learn, Spark ML are some of these.
Majority Deep learning frameworks provide a developer-friendly abstraction to create a network easily, take care of distributing computation and has support for GPUs.Caffe,Caffe2,Theano,Torch,Keras,CNTK,TensorFlow are popular frameworks.Tensorflow from Google is widely used now with active community support.
Supervised Learning vs Deep Learning Comparison table
Below is some key comparison between Supervised Learning vs Deep Learning
| The Basis of Comparison between Supervised Learning vs Deep Learning | Supervised Learning | Deep Learning |
| Model Training | Major tasks in training –
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Major tasks in training –
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| Potential of system | Used to solve relatively simple tasks where relation with input features and target is human detectable and feature engineering is direct. Eg :
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Deep learning can do really intelligent tasks like
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| Flexibility | Models are more flexible which helps to fine tune ML model easily. There are well-defined methods like grid search with cross-validation to find the right hyperparameters | Less flexible as there are many hyperparameters to tweak like a number of layers, the number of neurons per layer, the type of activation function to use in each layer, the weight initialization logic, and much more. |
| Feature Representation | Derived or abstract features to be created explicitly. For example polynomial features as input for a Linear regression model | Abstract data representation automatically generated in hidden layers. That is why a trained CNN neural network can detect a cat in an image. |
| Generative models | Not possible to generate anything original as automatic abstract data representation is not happening | Once trained, a specific type of deep neural network can generate say new images, songs, or texts. Those are called GNN(Generative neural network) or GAN(Generative Adversarial Networks)
Some implementation of this type of network is used to create even new fashion designs |
Conclusion – Supervised Learning vs Deep Learning
Accuracy and capability of DNN(Deep Neural Network)s has increased a lot in the last few years. That is why now DNNs are an area of active research and, we believe, it has the potential to develop a General Intelligent System. At the same time, it is difficult to reason why a DNN gives a particular output which makes fine-tuning a network really difficult. So if a problem can be solved using simple ML models it is strongly recommended to use that. Due to this fact, a simple linear regression will have relevance even if a general intelligent system is developed using DNNs.
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