Deep Learning

Course overview

What is the Course About?

This course is designed to provide you with a comprehensive understanding of the fundamentals of deep learning. Deep learning is a subfield of machine learning that uses neural networks to model complex patterns in data. This course covers the foundational concepts of deep learning, including neural networks, backpropagation, convolutional neural networks, and recurrent neural networks.

What Will You Learn?

You will learn the basics of deep learning, including the architecture of neural networks, activation functions, loss functions, and optimization algorithms. You will also learn how to build deep learning models using popular libraries such as TensorFlow. In addition, you will learn about convolutional neural networks and recurrent neural networks, which are used for image recognition and natural language processing, respectively.

Skills You Will Gain

After completing this course, you will have gained the following skills:

• Understanding of deep learning concepts and their applications
• Ability to build and train deep learning models using TensorFlow
• Understanding of convolutional neural networks and their applications in image recognition
• Understanding of recurrent neural networks and their applications in natural language processing
• Ability to apply deep learning models to solve real-world problems

Overall, this course will provide you with a strong foundation in the fundamentals of deep learning, giving you the necessary skills to build and deploy deep learning models for various applications.

Why should I enroll for this course?

Undertaking a training course in basic Deep Learning can provide numerous benefits, including:

1. Understanding of the fundamentals: A basic Deep Learning course can help you understand the fundamental concepts of neural networks, activation functions, optimization algorithms, and loss functions. This understanding can serve as a strong foundation for advanced Deep Learning concepts and techniques.
2. Improved problem-solving skills: Deep Learning involves developing algorithms that can automatically learn from data and improve performance over time. This process can improve your problem-solving skills as you learn how to tackle complex problems by breaking them down into simpler parts.
3. Increased career opportunities: Deep Learning is a rapidly growing field with numerous career opportunities. Undertaking a basic Deep Learning course can help you acquire the skills and knowledge necessary to pursue a career in this field, including roles such as Machine Learning Engineer, Data Scientist, and AI Researcher.
4. Enhanced data analysis skills: Deep Learning involves working with large datasets and analyzing complex patterns in data. A basic Deep Learning course can help you develop the skills necessary to analyze data more effectively, including data preprocessing, data cleaning, and data visualization.
5. Hands-on experience: Many basic Deep Learning courses involve hands-on projects and exercises that allow you to practice building and deploying Deep Learning models. This hands-on experience can help you develop practical skills and gain real-world experience in Deep Learning.

Overall, undertaking a training course in basic Deep Learning can provide you with numerous benefits, including a strong foundation in Deep Learning concepts, improved problem-solving skills, increased career opportunities, enhanced data analysis skills, and hands-on experience in building and deploying Deep Learning models.

Career Pathways, Average Salary and Hiring Companies: 

After undertaking a course in the fundamentals of Deep Learning, there are several career pathways you can pursue, including:

1. Deep Learning Engineer: A Deep Learning Engineer designs, develops, and implements deep learning algorithms and models that can learn from data and make predictions.The average salary for a Deep Learning Engineer in India is around ₹9,00,000 per annum. Major hiring companies include Amazon, Google, Microsoft, Nvidia, and Qualcomm.
2. Data Scientist – Deep Learning: A Data Scientist with a focus on Deep Learning is responsible for applying deep learning techniques to analyze and interpret large and complex data sets to extract insights and make informed decisions.The average salary for a Data Scientist with a focus on Deep Learning in India is around ₹9,00,000 per annum. Major hiring companies include Accenture, Deloitte, Fractal Analytics, Genpact, IBM, Infosys, KPMG, TCS, and Wipro.
3. Research Scientist – Deep Learning: A Research Scientist with a focus on Deep Learning is responsible for conducting research and developing new techniques and models for deep learning.The average salary for a Research Scientist with a focus on Deep Learning in India is around ₹12,00,000 per annum. Major hiring companies include Amazon, Google, Microsoft, Nvidia, and Qualcomm.
4. Machine Learning Engineer – Deep Learning: A Machine Learning Engineer with a focus on Deep Learning is responsible for developing and deploying machine learning algorithms and models that use deep learning techniques to make predictions.The average salary for a Machine Learning Engineer with a focus on Deep Learning in India is around ₹9,00,000 per annum. Major hiring companies include Amazon, Google, IBM, Microsoft, Nvidia, and Qualcomm.
5. Computer Vision Engineer: A Computer Vision Engineer is responsible for developing and implementing computer vision algorithms and models that can analyze and interpret visual data such as images and videos.The average salary for a Computer Vision Engineer in India is around ₹8,00,000 per annum. Major hiring companies include Amazon, Google, Microsoft, Nvidia, and Qualcomm.

Note that the above-mentioned salaries are only indicative and may vary depending on various factors such as location, experience, skillset, and company size.

Curriculum

Introduction to Machine Learning: This section provides an overview of machine learning and its applications. It covers the basic concepts of supervised and unsupervised learning, and introduces the tools and techniques used in the field.

Python Crash Course: This section covers the basics of Python programming, including variables, data types, loops, and functions. It provides the necessary foundation for working with data and building machine learning models using Python.

Numpy, Pandas, Tensorflow Crash Course: This section covers the most widely used Python libraries for data analysis and machine learning, including NumPy, Pandas, and TensorFlow. It covers the basics of these libraries and how they can be used to manipulate data and build machine learning models.

Introduction to Neural Networks and Deep Neural Networks: This section provides an overview of neural networks and deep learning. It covers the architecture of neural networks, activation functions, loss functions, and optimization algorithms.

Convolutional Neural Networks (CNN): This section covers convolutional neural networks (CNNs), which are commonly used for image classification and recognition. It covers the basics of CNNs, including the architecture of a CNN, convolutional layers, pooling layers, and fully connected layers.

Recurrent Neural Networks (RNN): This section covers recurrent neural networks (RNNs), which are commonly used for natural language processing and speech recognition. It covers the basics of RNNs, including the architecture of an RNN, long short-term memory (LSTM), and gated recurrent unit (GRU).

Autoencoders: This section covers autoencoders, which are used for data compression and dimensionality reduction. It covers the basics of autoencoders, including the architecture of an autoencoder, deep belief networks, and how to implement them in code.

Reinforcement Learning: This section covers reinforcement learning, which is a type of machine learning that involves an agent interacting with an environment to learn how to make decisions. It covers the basics of reinforcement learning, including the Markov decision process, Q-learning, and policy gradients.

Generative Adversarial Networks (GAN): This section covers generative adversarial networks (GANs), which are used for generating realistic images and videos. It covers the basics of GANs, including the architecture of a GAN, how to train a GAN, and how to generate new images using a GAN.