Artificial Intelligence

Top 5 Requirements for Deep Learning Projects

Market Trends

Deep learning has gained unprecedented success in computer vision. Deep learning is a term often used synonymously with machine learning and artificial intelligence but is not the same thing. Machine learning is a type of AI where a computer learns to do something without being programmed to do it. On the other hand, deep learning is basically a part of a broader family of machine learning methods based on artificial neural networks with representation learning. With deep neural networks, learning can be supervised, semi-supervised or unsupervised. Here we will talk about the top requirements for deep learning projects to help you prepare for learning its more complex ideas.

1.Learn an AI/ML/DL compatible language

 Learning a programming language is necessary if mastering Deep Learning techniques is your goal. Programming languages such as Python, R are preferred when it comes to learning AI, deep learning or machine learning. Choose a programming language and start learning to code right away.

2.Knowledge of Computer Science Fundamentals and Data structures

Unique software engineering skills like Data Structures, Software Development Life Cycle, and Github are required for developing machine learning or deep learning algorithms. Not all the times clients would want you to develop an ML model. At times they need a solution that may require a deeper knowledge of these concepts.

3.Mathematics for Machine Learning

Tuning algorithms to a specific requirement need a deeper knowledge of mathematical and statistical concepts. For training and inference tasks a good understanding of statistical concepts like Gradient Descent, distance matrics, mean, median, and mode, etc., are required.

4.Front End/UI Technology & Deployment services

Presenting the product to clients in the form of charts and visuals is as important as developing the product. To master this art, a deep learning engineer should acquaint himself with different UI technologies like Django, Flask and in certain cases, JavaScript.

5.Knowledge of Cloud Computing Platforms

Improving on AI/ML systems needs using data retrieval techniques at regular intervals. For data as large as zillions of bytes, cloud technologies are the go-to solutions and hence a reasonable understanding of cloud computing platforms.

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