Deep Learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain called artificial neural networks. 

These neural networks are layers of interconnected nodes, or artificial neurons, that allows the model to learn and make decisions that mimic how the human brain works. 

Deep Learning models are typically used for tasks such as image recognition, natural language processing, and speech recognition.

The future of machine learning is a matter of much discussion because of deep understanding. Technology is developing quickly, which causes both worry and enthusiasm. 

While most people are familiar with machine learning and artificial intelligence, deep learning is the "new kid on the block" in IT circles and is accompanied by enthusiasm and fear.

When artificial neural networks learn from substantial amounts of data, the process is known as deep learning, also called structured neural learning. 

Deep learning algorithms repeatedly complete tasks, adjusting the results each time. The algorithms' "learning" is fueled by enormous volumes of data.

Deep learning is made feasible by the fantastic amount of data produced each day, which according to current estimates, amounts to 1.145 trillion MB. 

The enormous increase in data generation drives the rise in deep learning skills. Although deep learning can seem mysterious, most of us use deep learning techniques daily.

Examples of Deep Learning

Some examples of tasks and applications where deep learning is used include:

• Image classification: using deep learning models to identify objects or features in images, such as recognising faces in photos or identifying traffic signs on the road.
• Object detection: using deep learning models to locate and identify multiple objects within an image or video.
• Natural Language Processing (NLP): using deep learning models for tasks such as language translation, text summarisation, and sentiment analysis.
• Speech recognition: using deep learning models to convert speech to text, such as in virtual assistants like Siri or Alexa.
• Generative models: using deep learning models to generate new data, such as images, text, or music.
• Self-driving cars: using deep learning models to enable cars to navigate, detect and respond to obstacles, and make decisions.
• Recommender systems: using deep learning models for the recommendation, such as movie or product recommendations.
• Medical imaging: using deep learning models to detect and classify medical images such as X-ray, MRI, and CT scans.
• Robotics: using deep learning models for tasks such as perception, grasping and manipulation, and decision-making.

These are just a few examples, but deep learning is applied to many fields and industries.

The Deep Learning Skills You Need

To work with deep learning, it is helpful to have a strong background in the following skills:

• Programming: Deep learning requires a good understanding of programming concepts, particularly in Python, the most commonly used language for deep learning.
• Linear Algebra: Linear algebra is the foundation of deep learning, as it is used to represent and manipulate the data and weights in neural networks.
• Calculus: Calculus is used to optimise the parameters in deep learning models.
• Machine learning: A good understanding of machine learning concepts and techniques is necessary to understand how deep learning fits into the broader field of machine learning.
• Data Preprocessing: Knowledge of data preprocessing is essential to prepare data for deep learning models.
• Neural networks: Familiarity with the basic concepts and architectures of neural networks, such as feedforward and recurrent networks, is necessary to understand how deep learning works.
• Frameworks and Libraries: Familiarity with popular deep learning frameworks such as TensorFlow, Keras, PyTorch, and Caffe will help implement deep learning models.
• Cloud and cluster computing: Knowledge of cloud and cluster computing will be helpful as deep learning models are computationally intensive and require a large amount of data to be trained.
• Experimentation and debugging: Deep learning model development is an iterative process that requires experiment and debugging to improve the model's performance.
• Domain knowledge: Knowledge of the specific domain you are working on is essential to understand the data and the problem you are trying to solve.

Some of these skills may not be required for all deep learning tasks, and they can be acquired by learning and experimenting with deep learning models.

Necessary Education For Deep Learning 

A solid foundation in mathematics, particularly in linear algebra, calculus, and probability, is necessary for understanding the underlying principles of deep learning. 

Additionally, knowledge of programming and experience with a programming language such as Python is necessary to implement and experiment with deep learning models. Familiarity with machine learning concepts and techniques is also helpful. 

A degree in computer science or a related field is a common educational path for those interested in deep learning. However, self-study and online resources can also be used to gain the necessary knowledge and skills.

Experience Required

Experience with machine learning and programming is essential for working with deep understanding. This can include experience with:

• Programming languages such as Python and frameworks such as TensorFlow or PyTorch
• Machine learning concepts such as supervised and unsupervised learning, regularisation, and optimisation
• Data preprocessing and cleaning, as well as experience working with large datasets
• Experience with cloud-based platforms for training and deploying deep learning models

Additionally, hands-on experience with building and training deep learning models is essential. This can be gained through participating in online competitions, doing personal projects, or working on projects as part of a team in an industry or research setting.

It's important to note that deep learning is a rapidly evolving field, and experience in one area may not necessarily transfer to another area. For example, experience in computer vision may not directly transfer to natural language processing.

Careers in Deep Learning

Deep learning has a wide range of applications, and as a result, many career opportunities are available for those with expertise in this field. Some common career paths include:

• Research Scientist: Deep learning research scientists work in academia or industry to develop new models and algorithms for various applications, including computer vision, natural language processing, and speech recognition.
• Data Scientist: Data scientists use deep learning techniques to analyse and interpret complex data and to build predictive models. They often work in industries such as finance, healthcare, and retail.
• Machine Learning Engineer: Machine learning engineers are responsible for designing, building, and deploying deep learning models in a production setting. They work closely with data scientists and software engineers to implement and scale deep learning models.
• Computer Vision Engineer: Computer vision engineers specialise in using deep learning to solve problems related to image and video analysis, such as object detection and tracking, facial recognition, and image generation.
• NLP Engineer: Natural Language Processing Engineers specialise in using deep learning to solve problems related to natural language understanding, such as text classification, machine translation, and question answering.
• Autonomous systems Engineer: Autonomous systems engineers use deep learning to develop self-driving cars, drones, and robots that can navigate and make decisions on their own.

These are just a few examples of profound learning career opportunities. The field is constantly evolving, so new opportunities are likely to emerge in the future.

Salary Outlook

Salaries for deep learning professionals can vary widely depending on location, industry, and experience level. 

However, deep learning professionals tend to command high salaries due to the high demand for their skills.

Here are some average salary ranges for some typical deep-learning roles based on data from the United States:

• Research Scientist: $100,000 to $150,000
• Data Scientist: $120,000 to $190,000
• Machine Learning Engineer: $120,000 to $180,000
• Computer Vision Engineer: $120,000 to $170,000
• NLP Engineer: $120,000 to $170,000
• Autonomous systems Engineer: $110,000 to $170,000

It's important to note that these are just averages and salary ranges can vary widely depending on location and industry. 

Additionally, many deep learning jobs are in the technology industry, where salaries are higher than in other sectors.

Having a graduate degree, a PhD or a postdoc in the field, and having experience and a good portfolio, can increase the salary range.