How to Become a Deep Learning Engineer?
Passionate about complex computer handling? Willing to exhibit your ability to combat any issue at the workplace? A rewarding journey as a Deep Learning Engineer awaits you. Offering much more to add adventure and to keep you active, the role is in high demand with expected exponential growth. Following a roadmap after recognizing your passion is crucial to remain on the right path and reach the vision. For understanding the path, a detailed stepwise guide is here to aid you in your journey.
Table of contents
- What is Deep Learning?
- What Does a Deep Learning Engineer Do?
- Deep Learning Engineer vs. Machine Learning Engineer
- Skills Required for Becoming a Deep Learning Engineer
- Deep Learning Engineer Toolkit
- How to Become a Deep Learning Engineer?
- Deep Learning Engineer Salary
- Frequently Asked Questions
What is Deep Learning?
Deep learning is a subfield of machine learning concerned with training artificial neural networks in aspects like learning and prediction. The domain finds specific applications in the processing of large-scale and complex data. It is used to train the computer through supervised and unsupervised learning. It also teaches data evaluation.
The multi-layered structure of interconnected nodes, resembling the human brain, runs the deep learning models. These structures aid in automatic learning, extraction of meaningful features from raw data and allowing better performance compared to other machine learning models.
What Does a Deep Learning Engineer Do?
The deep learning job requires candidates to perform the following duties:
- Project handling: System design, architecture, and deployment. It includes selecting appropriate network architecture, for instance, convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformers, depending on the problems.
- Data processing: Gathering, cleaning, organizing, and evaluating data. It also includes handling, labeling, formatting, and preparing data for training through other stated methods.
- Model training: Training models using prepared or processed data. It includes selecting suitable architectures, fine-tuning hyperparameters, and model performance optimization. They include frameworks like Keras, PyTorch, and TensorFlow and leverage GPU to accelerate the training process.
- Evaluation and Validation: To cross-check the trained models’ performance and accuracy. It makes use of evaluation metrics and testing.
- Hyperparameter search: Finding the optimal settings or environment for the best performance involves techniques like optimization algorithms, regularization, and hyperparameter tuning to improve the model’s speed, accuracy, and efficiency.
Deep Learning Engineer vs. Machine Learning Engineer
The difference between machine learning and deep learning jobs is enlisted below:
|Deep Learning Engineers||Machine Learning Engineers|
|Focus on complex model development for large, high-dimensional, and intricate calculations.||Focus on ML algorithms including support vector machines, decision trees, and deep learning algorithms.|
|Tasks include natural language processing, generative modeling, image recognition, and speech recognition.||Tasks include feature engineering, data preprocessing, hyperparameter tuning, model selection, and deploying ML models.|
|Work with complex architectures, patterns, and hierarchical representations to automatically learn intricate features.||Work with traditional ML algorithms like random forests, support vector machines, linear regression, or gradient boosting.|
|Deal with large-scale and high-dimensional data such as sensor data, text, audio, and images.||Handle various data dimensions and sizes, including structured, sparse, or tabular data.|
|Industrial applications in speech recognition, autonomous systems, natural language processing, and deep reinforcement learning.||Industries include fraud detection, customer segmentation, recommendation systems, predictive maintenance, sentiment analysis, and time series analysis.|
Skills Required for Becoming a Deep Learning Engineer
The deep learning job requires the below-mentioned skills:
- Probability and Statistics: Bayes theorem, sampling and hypothesis testing
- Linear algebra: Matrices, vectors
- Calculus: Differential and integral calculus
- Gradient descent and distance matrices
Data Engineering Skills
- Data pre-processing: Cleaning, parsing, correcting ,and consolidating
- ETL (Extraction, Transformation, Load)
- Knowledge of database
Machine Learning Algorithms
- Knowledge about algorithms such as apriori, support vector machine, K nearest neighbor, decision tree, random forest, K means clustering and others
- Algorithms: ANN, RNN, CNN, Generative Adversarial Network, Deep Belief Network and Long Short Term Memory Network
- Their applications and model creation using a single or combination of these algorithms
- Frameworks: TensorFlow, Keras, DL4J, PyTorch, CNTK
Front/UI Technology Skills
- Time management
- Independent problem solving
- Analytical skills
- Critical thinking
Deep Learning Engineer Toolkit
Some of the most known abilities expected from a candidate interested in acting as deep learning engineer are:
- Excelling in the usage of programming languages due to their rich collection of libraries and ease of usage. Must be able to work with Keras, NumPy, TensorFlow, and PyTorch.
- Must be familiar with GPU-accelerated computing through CUDA for faster training and inference of deep learning models.
- Familiarity with Jupyter Notebook for creating and sharing code containing documents, explanations and visualizations.
- Ability to code, debug and manage deep learning projects on Integrated Development Environments (IDEs) such as Visual Studio Code, PyCharm or Spyder.
- Data processing and manipulation by tools like Pandas for data loading, cleaning, transformation, and analysis.
- Visualization and plotting using Matplotlib and Seaborn and model evaluation and metrics using Scikit-learn.
How to Become a Deep Learning Engineer?
The stepwise pathway to gain a deep learning job is as follows:
- Basics: Begin with building a foundation in mathematics and programming languages. Gain knowledge of data structures, programming concepts and algorithms.
- Machine Learning, Neural Networks and Deep Learning: Learn associated principles, algorithms, architecture and types of neural networks, activation functions, types of learning and other related concepts.
- Programs: Gain expertise in the fields by learning under supervision and expert guidance. Familiarize with current trends and attend workshops, conferences, trends and advancements.
- Practical experience: Find opportunities to gain hands-on experience, build connections and inculcate skills. Work on tools for projects and familiarize yourself with real-world problems. Discover methods to handle problems in the domain. Focus on networking to remain informed about new opportunities and never leave them.
- Build a portfolio: Exhibit your learning, skills and specialties through the portfolio. Showcase your works, explain the problem statements, your approach and contribution to the solo and group projects and the quantitative aspects of results.
- Continuous learning: Remaining updated and keeping yourself upskilled is one of the best things you can do to excel in the field. Pursue advanced education if required.
Deep Learning Engineer Salary
Becoming a deep learning engineer requires a strong foundation and consistent action to become an expert in the field. The right set of actions in a specific direction tends to aid in achieving the goals. To become a deep learning engineer, the accurate method is to begin the journey with a comprehensive curriculum designed specifically for deep learning, computer vision, NLP, and other concepts.
Covering various tools like Python, Pandas, Matplotlib, NumPy and much more, Analytics Vidhya offers placement assistance, weekly mentorship calls, a personalized roadmap to guide you in creating your success journey and much more. Another important aspect, more than 50 real-world projects are also available in the AI & ML Blackbelt Plus program. Have a look for comprehensive details and connect for better guidance and information.
Frequently Asked Questions
Ans. The time required to become a deep learning engineer varies depending on your level of education, field of work, expertise level and learning power. One requires foundational knowledge and practical experience to enter into internships or fresher-level jobs, followed by promotion to senior levels.
Ans. Understanding mathematics, computer science, programming languages and data handling is required in technical skills. Further soft skills like problem-solving, analytical and critical thinking approach and communication also are crucial to becoming a deep learning engineer.
Ans. The salary varies according to the country. The average earning in India is around INR 8 lakhs per year, excluding additional cash compensation.
Ans. Deep learning engineers are concerned with model design and architecture, data preprocessing and cleaning, model training and optimization, hyperparameter tuning and performance evaluation.