Roadmap for Deep Learning
4 min read
Table of contents
- Applications of Deep Learning
- Carrier Paths in deep learning
- Skills needed to be acquired
- 1. Basic Mathematical skills
- 2. Programming skills
- 3. Data Engineering
- 4. Knowledge of Machine learning
- 5.Deep Learning algorithms
- 6.Knowledge of Deep learning Frameworks
- 7. Projects
- "The more you work on projects, the more you will learn."
Deep learning is a branch of machine learning and artificial intelligence that uses advanced computations to model the structure and operation of the neural networks in the human brain. In order to automatically extract and learn complicated representations of data patterns, hierarchical features, and complex abstractions from large-scale datasets, it revolves around training and using deep artificial neural networks, which are typically made up of multiple layers of interconnected nodes or artificial neurons.
Applications of Deep Learning
Deep learning has various applications specially in this day and age as it is growing day by day there is so much to explore.
Some of the commonly used applications that are commonly known are:
Natural Language Processing
Autonomous Systems(autonomous systems like self-driving cars and drones. It helps in perception tasks, object detection, and decision-making by processing real-time sensor data and generating appropriate responses.)
Carrier Paths in deep learning
Artificial intelligence and machine learning are promising employment fields with prospects for lifelong development. As a result, the field of machine learning and deep learning is very expansive and seems promising in terms of career possibilities and pay.
Software developer with specialization in Data Science
Skills needed to be acquired
An effective machine learning method is deep learning. Consequently, developing deep learning models calls for extensive machine learning expertise. Let's examine some of the crucial abilities you'll require to master deep learning:
1. Basic Mathematical skills
Understanding how deep learning algorithms function requires a solid foundation in mathematics, particularly statistics. Some of the requitred mathematical skills are:
2. Programming skills
Efficiency in at least one language is needed, While many programming languages can be used in machine learning, some of the most popular ones include Python and R. These high-level programming languages come with libraries and packages that simplify your work further.
3. Data Engineering
Having a solid foundation in data engineering is essential since deep learning uses a lot of data.
Data engineering may include skills like:
- Data Pre-processing
- Managing huge databases(knowledge of Oracle, MySQL, and NoSQL databases.)
- Data extraction, transformation and load (ETL)
4. Knowledge of Machine learning
If you want to grasp deep learning, you must be familiar with machine learning algorithms.
Some Ml algorithms you should be familiar with are:
Support Vector Machine.
K nearest Neighbour.
K means Clustering.
5.Deep Learning algorithms
Deep learning algorithms are a vital component of any deep learning toolkit.
Some DL algorithms you should be familiar with are:
Artificial Neural Networks (ANN): Neural networks are the foundation of deep learning.
Convolutional Neural Networks (CNN): CNNs are widely used for image recognition and computer vision tasks.
Recurrent Neural Networks (RNN): RNNs are designed to process sequential data, such as natural language or time series data, by utilizing feedback connections.
Long Short-Term Memory (LSTM): LSTMs are a type of RNN that addresses the vanishing gradient problem.
Generative Adversarial Networks (GAN): GANs consist of two neural networks, a generator and a discriminator, that are trained in an adversarial manner.
Autoencoders: Autoencoders are neural networks trained to reconstruct their input data at the output layer.
Deep Belief Networks (DBN): DBNs are composed of multiple layers of restricted Boltzmann machines (RBMs) and can be used for unsupervised pre-training or as generative models.
Reinforcement Learning (RL): Although not exclusive to deep learning, RL algorithms can benefit from deep neural networks as function approximators.
After learning these algorithms you should also know how to :-
- Select a Problem. - Choose an appropriate algorithm for your problem. - Create a model with one or more algorithms. - Optimize your model for the best accuracy.
6.Knowledge of Deep learning Frameworks
You must become familiar with the various deep learning frameworks that support deep neural network creation, training, and validation.
Some DL framework you should be familiar with are:
Microsoft Cognitive Toolkit.
Making projects and learning from them is the essential part because without this step you can not learn deep learning or require those skills that you want.
"The more you work on projects, the more you will learn."
Some projects you can start making at the beginner level are: