Whether you are a data analyst, data engineer, software developer, or simply someone interested in these careers or fascinated by the world of AI and aspiring to thrive in an AI-related career path, deep learning is an essential topic for you to explore.
Having a profound understanding of AI, to the extent of building deep learning models, can provide a significant advantage in the fields of AI and data.
In a nutshell, deep learning refers to an AI model that emulates our neural networks, allowing it to learn autonomously, much like how our brains function.
Now, you might be envisioning the numerous possibilities that deep learning can unlock. If you're eager to delve deeper into the world of deep learning, Sertis has compiled a comprehensive deep learning roadmap to guide you in getting started. Let's jump right in!
1. Sharpen Your Mathematical Skills
To embark on your deep learning journey, it's crucial to strengthen your mathematics foundation, as it forms the basis for understanding deep learning algorithms. Begin by mastering the following key topics:
Statistics & Probabilities: Bayes theorem, probability distribution, and sampling and hypothesis testing
Linear algebra: Matrices and vectors
Calculus: Differential calculus and Integral calculus
2. Programming Languages
In your journey to master deep learning, a fundamental skill is proficiency in programming languages. While there are numerous programming languages to choose from, the ones widely used in deep learning include Python, R, C, and Java.
Among these options, Python and R are considered the most suitable programming languages for deep learning. Python, in particular, stands out for its ease of learning due to its straightforward syntax and the abundance of available frameworks, making it a convenient choice for deep learning tasks.
3. Data wrangling
Data wrangling is a critical skill set that involves managing, transforming, and organizing data. This skill is essential because deep learning often involves working with vast amounts of data. Data wrangling skills are necessary to prepare the data for model training.
Data wrangling encompasses the following key topics:
Data Pre-processing: This includes tasks like data preparation, cleaning, selection, and transformation.
ETL (Extract, Transform, Load): ETL is the process of extracting data from various sources, transforming it into a suitable format, and loading it into the destination system.
Databases: Familiarity with databases, such as MySQL, Oracle, and NoSQL, is crucial for efficiently handling and managing data.
4. Machine learning algorithms
Another crucial foundation for deep learning is a solid understanding of various types of machine learning. To excel in deep learning, it's essential to have a good grasp of machine learning concepts. You should familiarize yourself with the following types of algorithms:
Classification algorithms: Naive Bayes, Decision Trees, and Random Forest
Regression algorithms: Linear Regression, Decision Trees for Regression, and Support Vector Regression (SVR)
Clustering algorithms: K-Means Clustering and Hierarchical Clustering
5. Deep learning algorithms
Now, let's delve into a crucial skill – understanding the various types of deep learning algorithms. This knowledge is vital for comprehending how they function and for selecting the most appropriate algorithm for your specific tasks. The key deep learning algorithms you should familiarize yourself with include:
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
Generative Adversarial Network
Deep Belief Network
Long Short-Term Memory Network
Additionally, it's important to practice the following skills:
Selecting an appropriate problem and matching it with the right algorithm.
Building models using one or more of these algorithms.
Optimizing your models to achieve the highest level of accuracy.
6. Deep learning frameworks
Deep learning frameworks are invaluable tools that streamline our work and save us from writing every line of code from scratch. Currently, there is a wide range of open-source deep learning frameworks available that we should familiarize ourselves with to optimize our work.
Here are some essential deep learning frameworks to learn:
TensorFlow
Theano
scikit-learn
PyTorch
Keras
Deeplearning4j (DL4J)
Caffe
Microsoft Cognitive Toolkit
7. Working on projects
There's no better way to learn than through practical experience. In the final step of this roadmap, we emphasize working on your own deep learning projects. This hands-on approach allows you to practice with real projects and build a portfolio that will be valuable for future job applications.
Consider embarking on projects like:
Face Recognition
Image Classification
Cat vs. Dog Classifier
Handwritten Digit Recognition
Spam Email Detector
Learning from working on a variety of projects, assessing results, identifying shortcomings, and striving for improvement is essential.
Continuous and determined practice is the only path to becoming a deep learning expert. Keep up the great work!
Sertis is a vibrant community of individuals passionate about data and AI, united in the pursuit of success in the future world. To learn more about Sertis, our teams, culture, and job opportunities, visit us at: https://www.careers.sertiscorp.com/