Top 8 Deep Learning Concepts Every Data Science Professional Must Know

“Deep learning is making a good wave in delivering a solution to difficult problems that have been faced in the field of artificial intelligence (AI) for so many years, as quoted by Yann LeCun, Yoshua Bengio & Geoffrey Hinton.”

For a data scientist to successfully apply deep learning, they must first understand how to apply the mathematics of modeling, choose the right algorithm to fit your model to the data, and come up with the right technique to implement. 

In order to get you started, we have come up with a list of deep learning algorithms needed by every data science professional.

1. Cost Function

The cost function used in a neural network is almost similar to the cost function used in any other machine learning model. This helps identify how good your neural network is as compared to the value it predicts (when compared to the actual value). 

Simply said, the cost function is inversely proportional to the model’s quality. For example, the better the quality of your machine learning model, the lower the cost function it will be or vice versa. 

The major significance of the cost function is to obtain the right value for optimization. When the cost function of a neural network is minimized, you can easily achieve the optimal parameters and weights of the model. In this manner, you can expand its performance. 

Some of the most common cost functions include exponential cost, Kullback-Leibler divergence, cross-entropy cost, Hellinger distance, and quadratic cost. 

2. Activation Functions

Being a data science professional, you need to know the basics of neural networks and how they function. Once you gain an in-depth understanding of nodes and neurons, understanding activation is not tough. Understanding activation is as simple as pressing the light switch, it helps decide whether you need to activate the neuron. 

Although you may find multiple activation functions available, one of the most common activation functions is the “Rectified Linear Unit function.” Also, referred to as the ReLu function, this function accelerates gradient descent thus making it much faster. 

3. Recurrent Neural Networks (RNN)

As the name suggests, the RNN works perfectly great with sequential data. Why? Because it can ingest inputs with the difference in sizes. 

The RNN considers both the current input given as well as the previous inputs given. This means the same input can also produce different outputs even though the input is the same. 

In technical terms, RNNs are defined as the type of neural network that has a connection which further forms a digraph all along the temporal sequence. This connection needs to happen between the nodes further allowing to utilize the internal memory and process the variable-length sequences of the inputs. 

RNN is perfect for time-series data or sequential data. 

4. Backpropagation

Backpropagation closely syncs with a cost function. This algorithm is used specifically for computing the cost function’s gradient. Due to its speed and efficiency, backpropagation managed to gain a lot of popularity as compared to other approaches. 

Here’s how backpropagation works:

• Calculate forward phase for individual input and output pair
• Calculate the backward phase for each of the pair
• Both the gradients are combined 
• Updates on the weights are done according to the total gradient and the rate of learning  

5. Long Short-Term Memory Networks (LSTM)

LSTM networks come under the category of RNNs (Recurrent Neural Network). The LSTM is used to address shortcomings of regular RNNs since they have short-term memory. 

More so, if there’s a lag in the sequence that is larger than 5 to 10 steps, the RNN often dismisses any information provided in the previous steps. 

For instance, if you have fed information like a paragraph into the RNN, the information might be different as compared to the information that was given at the start of the paragraph. Therefore, making LSTMs a better option to solve such issues. 

You’ll find endless information data science offers, however, having extensive knowledge in areas like machine learning and deep learning is mandatory for every data scientist. There are multiple data science certificate programs available online that teach you the fundamentals of machine learning. You can grab anyone that fits your requirement and get started with your learning journey. 

6. Convolutional Neural Networks (CNNs)

CNN usually picks up an input which is an image, allot significant features of the image, and then makes the prediction. CNN is much better than the feedforward neural networks due to the way it captivates spatial dependencies from the image. Simply said, CNN understands the image’s composition much better than any other neural network. 

Specifically, CNNs are used to classify images. 

7. Hyper-parameters

These are variables that help in regulating network structure that rules the way the network is trained. Some of the common hyper-parameters are – the learning rate (alpha), batch size, number of epochs, network weight initialization, and model architectures like a number of hidden units or number of layers. 

8. Batch and Stochastic Gradient Descent

Both methods are used for computing the gradient.

Batch gradient descent is used to compute the complete dataset whereas stochastic gradient descent computes only a single sample at a time. 

As a result, batch gradient descent is ideal for convex or smooth manifolds while stochastic gradient descent is great with faster computation and it is inexpensive. 

In a Nutshell

Deep learning has caused a greater impact on data science. Largely affected by AI, deep learning uses a large amount of unsupervised data to extract any complex representation. As a result, it helps observe, analyze, learn, and make decisions even in the toughest situations.