Introduction
The understanding problem is the most important aspect in the modelling process of Deep Learning as it provides a clear understanding of the problem that can help you to choose the correct model type of deep learning, design the network architecture, and make decisions about the data preprocessing and evaluation metrics.
Deep Learning Applications
Deep Learning can be fused into the application of various fields whether it is healthcare services, autonomous systems, financial services, robotics, marketing, and many more.
Computer vision
Apply CNNs for image classification, image segmentation, and object detection.
Natural Language Processing
Apply RNNs and transformers for language translation, sentiment analysis, and text classification.
Speech Recognition
Deploy tasks like speaker identification and speech-to-text.
Recommender system
Acquire and analyze purchase history and suggest products to users.
Gaming
Apply artificial intelligence to teach game agents through reinforcement learning.
Financial Services
Deploy tasks like fraud detection, algorithmic trading, and risk assessment.
Deep Learning Process
Step-1
Understanding Problem
The understanding problem is the most important aspect in the modelling process of Deep Learning as it provides a clear understanding of the problem that can help you to choose the correct model type of deep learning, design the network architecture, and make decisions about the data preprocessing and evaluation metrics.
Step-2
Data Collection
The next step is to collect a large dataset which represents the problem that needs to be solved. This dataset will be helpful to train the deep learning model.
Step-3
Data Preprocessing
The next step involves preprocessing of data to prepare it for use through the deep learning model. It commits to data cleaning, data transforming into a suitable format, and data normalizing to ensure that it drops under a fixed range of values.
Step-4
Model design
In the next step deep learning model design will be performed. It consists of choosing the best-suited neural network architecture for the selected problem and defining the structure of the networks in terms of the number of layers, the network size, and the type of activation functions.
Step-5
Model Training
After designing the model, the next step is to train the designed model on the preprocessed data. This involves data feeding through the network and model parameter adjustment for minimizing the error between predicted outputs and the actual outputs. It involves multiple iterations, with the model being modernized after each iteration for reducing the error.
Step-6
Model Evaluation
The next step is performance evaluation on the validation dataset, which is a separate dataset assigned for this purpose. The performance of the model is evaluated according to the metrics like accuracy, recall, F1-score, and precision.
Step-7
Model Deployment
If the performance of the model is satisfactory, next the model gets deployed in a real-world setting. It consists model integrating into the existing application or system, as well as deploying it on a suitable hardware platform.
Step-8
Model Maintenance
Next, the model performance is monitored and the necessary changes are updated for ensuring better performance.
Types of Deep Learning Techniques
Convolutional Neural Networks (CNNs)
CNNs are usually used for image and video recognition tasks, as well as they are designed to diagnose spatial patterns in data.
Recurrent Neural Networks (RNNs)
RNNs are used for handling sequential data, such as natural language, speech, and time-series data.
Generative Adversarial Networks (GANs)
GANs are used for producing new data by learning the fundamental distribution of the training data.
Autoencoders
Autoencoders are used for unsupervised learning and feature extraction by compressing and reconstructing input data.
Deep Belief Networks (DBNs)
DBNs are used for unsupervised learning and feature extraction, and they involve multiple layers of Restricted Boltzmann Machines (RBMs).
Reinforcement Learning
Reinforcement learning is used for teaching agents to cooperate with an environment and learn through trial and error.
Transformer Networks
Transformer networks are used for natural language processing tasks, such as language translation, by processing input sequences in parallel.