Gauri Tripathi
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Gauri Tripathi's Blog


Roadmap for Deep Learning

Gauri Tripathi's photo
Gauri Tripathi
·May 26, 2023·

4 min read

Table of contents

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

  • Speech Recognition

  • Targeted Marketing

  • 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.)

  • Healthcare


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.

  • Data Engineer

  • Data Scientist

  • Data Analyst

  • Research Scientist

  • Natural Language

  • Software developer with specialization in Data Science

  • AI consultant

  • AI architect


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:

  • Linear Algebra

  • Probability Theory

  • Statistics

  • Calculus

  • Algorithms


  • Optimization

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:

  • Naive Bayes.

  • Support Vector Machine.

  • K nearest Neighbour.

  • Linear Regression.

  • Logistic Regression.

  • Decision Tree.

  • Random Forest.

  • K means Clustering.

  • Hierarchical Clustering.

  • Apriori.

  • 1_7wQ

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:

  • TensorFlow.

  • Theano.

  • scikit learn.

  • PyTorch.

  • Keras.

  • DL4J.

  • Caffe.

  • Microsoft Cognitive Toolkit.

7. Projects

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:

  • Face Detection

  • Image Classification

  • Color detection

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