Guide to Deep Learning for Beginners

Deep learning has become a commonly used term for many AI developers these days. It is a complex subject that many data scientists spend their lifetime perfecting. For many who want to switch careers and become data scientists or have a little knowledge about artificial intelligence (AI), deep learning is a difficult subject to dive in to. This guide is designed for those who want to spark their interest in deep learning and introduce themselves to the basics.

What is Deep Learning

Deep learning (also known as deep machine learning) is a category of machine learning based on algorithms that attempts to model high-level abstractions in data. In simple terms, an AI program based on deep learning has one neuron that receives an input signal and another that sends output signals. An input layer receives an input and passes an updated version of that input to the next layer. In deep learning, many layers are present between input and output that allows algorithms to utilise multiple processing layers. All these layers have various linear and non-linear transformations.

Types of Deep Learning

For those new to AI programming using deep learning algorithms, there are three categories of deep learning model:

  1. Supervised Learning

In this type of group, the machine has a supervisor that provides it with answers. Data is already labelled, and the machine uses these labels and examples to learn, the results of which can be applied to future cases. An example is made up of input (vector) and output object (the supervisor). The learning of algorithm is picked from the labeled training data and then produces an inferred function used to map new objectives. This is the most common type preferred by AI programmers and developers. Classification tasks are most dependent on supervised learning that includes:

  • Identification of objects in images
  • Speech and language recognition
  • Spam detection and analysis of sentiments

 

  1. Semi-Supervised Learning

Semi-supervised learning is the learning made from labeled information combined with what has been observed from unlabeled information. An example of this type is a child growing up while learning from the parent (labelled information) and combining it with their observations like house, trees, roads (unlabelled data). AI programming based on semi-supervised learning uses both labelled and unlabelled data for training in which the majority of data is unlabeled with a few instances of labelled data. Semi-supervised learning can be classified as:

  • Transductive: Infers the correct labels for the given data, or
  • Inductive: Infers correct mapping from point one to another

 

  1. Un-Supervised Learning

Un-supervised in deep learning, AI programming is when machines learn the relationship between the elements in a dataset on their own. The data is then classified without the help of labels, and the algorithm looks for patterns or hidden features to analyse the data. Most common algorithms include:

  • Anomaly detection
  • Clustering
  • Neural networking

 

The Architecture of Deep Learning

  1. Generative Deep Architecture: This architecture is intended to characterize the high order correlation properties in observed data to analyse the patterns. It also describes the joint distributions of visible data and associated classes.
  2. Discriminative Deep Structure: It provides direct discriminative power for pattern classification and often characterises on prior distributions of classes of visible data.
  3. Hybrid Structures: In AI development, the goal here is to discriminate and assist the outcome of generative structure with better optimisation and regulation.

Deep Learning or Machine Learning

In AI development, deep learning is a subset of machine learning. People often get confused about using one over the other. When it comes to large datasets, deep learning works best, while machine learning is mostly used for small datasets. Another difference is that deep learning algorithms need time to train due to the broad parameters. On the other hand, machine-learning algorithms can train within a few hours. Interpreting ability is also a reason companies use machine learning in their AI programming. 

Final Word

In our world, an estimated 1 quintillion bytes of data are presently generated every day. As the information grows, so does the aspect of deep learning. The resulting neural networks can analyse and discover patterns within the amount of unstructured data present. If you’d like to gain experience while working towards your PhD in Computer Science, keep in mind that AI Seed offers helps place such PhDs in AI or machine learning startups.

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