Essential Deep Learning Skills

Deep learning is an advanced form of machine learning that utilizes neural networks to replicate human decision making which drives artificial intelligence (AI) applications. According to DeepLearning.AI, “Today, deep learning engineers are highly sought after, and deep learning has become one of the most in-demand technical skills as it provides you with the toolbox to build robust AI systems that just weren’t possible a few years ago. Mastering deep learning opens up numerous career opportunities” [1]. Explore the various components within deep learning and develop an understanding of the skills necessary to excel in deep learning tasks. 

Core competencies in deep learning

Deep learning consists of various components, including neural networks, machine learning frameworks, and hyperparameter tuning. Discover each in more detail to gain a deeper understanding of deep learning.

Understanding neural networks and architectures

The three main types of neural networks within deep learning models are: 

1. Convolutional neural networks (CNNs): You can use these networks for computer vision and image classification tasks. They can identify patterns within videos and images using pattern recognition, image recognition, face recognition, and object detection. 

2. Recurrent neural networks (RNNs): These networks are best for speech recognition and natural language tasks. They use data history to make predictions about future outputs. They also use natural language processing (NLP), language translation, speech recognition, and image captioning to make predictions. 

3. Generative adversarial networks (GANs): These networks develop new data that mirrors their training data. They consist of two components. The first, the generator, produces a hyper-realistic image. The second component, known as the discriminator, assesses the image to figure out if it’s real or not. The goal of the generator is to produce an image that is so realistic that the discriminator believes it’s genuine. 

Familiarity with frameworks

Deep learning is a subset of machine learning (ML) that processes information within ML models. ML frameworks are a key component within deep learning. 

TensorFlow is an ML framework you can use to develop both ML models and applications powered by AI. It offers ample resources on ML model development, including tutorials to help you throughout the process. 

Keras is an ML framework you can utilize to deploy ML applications. It provides debugging and code deployability tasks to ensure seamless integration. You can learn more about how to utilize Keras via their website’s developer guides. 

Knowledge of hyperparameter tuning

Hyperparameter tuning refers to the process of training ML models by setting hyperparameters, a type of variable used to coordinate ML model training that typically remains constant throughout the process. You can implement hyperparameter tuning manually or automatically. Manually tuning takes more time, but you can gain a better understanding of how the hyperparameter weights affect the ML model. If you prefer not to tune manually, various automated tuning methods are available. As you tune, you should track the results of your testing to gauge which set of hyperparameters produces the best result. 

Programming proficiency

To better understand deep learning, consider expanding your knowledge of programming languages like Python and R, as well as neural network circuits such as graphics processing units (GPUs). 

Proficiency in Python and R

Python is a data-focused programming language you can use for predictive analytics and data management. It can work with large volumes of data while training deep learning models. Python is one of the most widely used programming languages due to its adaptability and accessibility. It seamlessly integrates with various NLP libraries, making it one of the easiest programming languages to learn. 

R is a programming language used for predictive analytics, data analysis, and visualization. Finance, health care, and manufacturing are among the chief industries that use R due to the language’s forecasting capabilities and statistical analysis. R is another popular programming language, one that you can download and use for free since it’s open source. Additionally, it can run on various platforms. 

Familiarity with GPU programming

GPUs are circuits within deep learning networks that efficiently process data memory to accelerate image development. You can utilize these circuits to train complex deep-learning applications with large data sets such as gaming, video processing, or cryptocurrency mining. GPUs can perform thousands of tasks simultaneously within various neural networks. 

Analytical skills

The analytical skills necessary to help you excel in deep learning tasks include model performance evaluation. This requires an in-depth understanding of overfitting and underfitting within ML models. 

Evaluating model performance

Model performance evaluation consists of various steps to test models and ensure the production of valuable outputs. These steps include: 

• Select suitable metrics to verify data. When implementing evaluation metrics, for example, use accuracy, precision, recall, and F1-score metrics. 

• Test data sets and utilize cross-validation to prevent overfitting. Through these processes, you can estimate the average performance and variance of your model based on various data samples. 

• Compare your model to baselines and high-quality models. You can then see how well your model is working and determine where you can make improvements. 

• Assess errors and determine outputs to understand your model and interpret feedback. Examine incorrect outputs and determine why they were wrong so you can fix the mistakes. 

• Experiment and evaluate to improve model performance and develop the best outputs that you can. Experimentation may include hyperparameter tuning to see what produces the best results. 

Understanding overfitting and underfitting

Overfitting occurs when ML models produce inaccurate outputs for new data. If the training data size is too small, the training data is too large with a lot of irrelevant data (noisy data), the model is trained on one set of data for too long, or the model is too complex and learns on irrelevant or noisy data, these are all aspects that could cause overfitting. This is why model performance evaluation is a very important step so your model does not produce inaccurate outputs. 

Underfitting is another error within ML models. It happens when the model can’t make a correlation between the input and output, producing incorrect outputs. Underfit models occur when the model is trained for the incorrect amount of time on a large data set. 

Communication skills

Necessary communication skills to help you excel in deep learning positions include the ability to explain complex concepts and collaborate with other data professionals. 

Explaining complex concepts

When working with advanced topics, such as deep learning, it’s important to be able to communicate this information to teammates and managers. This is especially true if you’re hoping to reach a management position, as you will be consistently explaining complex concepts. Some tips on how to explain complex information include: 

• Know your audience 

• Provide various examples and context

• Keep your language as simple as possible 

• Use visuals and metaphors 

Collaborating with other data professionals

Collaboration with other data professionals is an important aspect of expanding your understanding of deep learning concepts. Finding mentors and potential internships and attending AI events with other data professionals will help you expand your expertise and develop relationships within the field. 

Build deep learning skills and knowledge with Coursera

Deep learning is a crucial component of developing machine learning models and driving AI applications. Learn how to develop deep neural networks, train and test deep learning models, and build neural networks in TensorFlow on Coursera with DeepLearning.AI’s Deep Learning Specialization, or expand your knowledge of machine learning development with Stanford and DeepLearning.AI’s Machine Learning Specialization.