Develop proficiency in the fundamentals of Python language, data time, structures, and control along with key libraries that are used in the development of Deep Learning models. The course first establishes you with base programming the language, syntax, and data structures including the use of lists and dictionaries and control structures like loops and conditional. Furthermore, of the generic AA, you will proceed to specific libraries like NumPy for handling numerical operations, TensorFlow for defining and training neural networks, and PyTorch based on dynamic computational graphs and model adjustability. After completing this course, you will be able to preprocess data, build and train deep learning models as well, and check the quality of the results achieved, using Python, for various purposes of deep learning.

Mathematics Foundations for Deep Learning:

Understand the mathematical foundations of Deep Learning algorithms with a focus on Linear Algebra, Calculus, and Optimization. This course begins with Linear Algebra about vectors, matrices, and operations required in constructing neural networks. You will then proceed to Calculus where you will learn about derivatives and integrals which are vital in backpropagation and gradient descent. The course also covers optimization techniques to give an understanding of how stochastic gradient descent and Adam optimizer help train the Deep Learning models efficiently. Thus, at the end of this course, you will have a strong mathematical background necessary for working with deep learning algorithms.

Building Deep Learning Models with Python:

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The course starts from basics where basics of Neural Networks, Perceptrons, Multi-Layer Perceptrons (MLPs), and activation functions are explained from where it is easy to understand the learning and prediction abilities of these models. Then, you will explore the advanced CNN architectures, which are the most fundamental for image recognition and computer vision to apply to the industries of Nepal including agriculture and medical imaging. Last but not least, you will learn about RNNs particularly for tasks such as text classification, natural language processing (NLP), etc. with special reference to Nepali languages.

Deep Learning Training & Optimization:

These are the Convolutional Neural Networks (CNNs) and the Recurrent Neural Networks (RNNs). The course begins with fundamental knowledge regarding Neural Networks, Perceptrons, Multi-Layer Perceptrons (MLPs), as well as activation functions to know about the learning and predicting factors of these models. Then you will look at the advanced CNN architectures because these are the most basic for image recognition and Computer Vision to use for the industries of Nepal including agriculture and medical imaging. Last but not least, let you know about the RNNs, especially for tasks like text classification, natural language processing, etc with special reference to Nepali languages. When you complete this course you should be in a position to design, train, and implement deep learning models for particular sectors in Nepal.

Career Opportunities

Become an AI professional in Nepal with this detailed deep-learning course. By completing this course you will be prepared to not only create and train high-performing Deep Learning models but also fine-tune them for practical use. Discover basic and advanced algorithms in handling of data, how to train models, and all about regularization. Moreover, it covers the issues related to the application of Deep Learning models in the Nepalese environment. We will also look at the challenges of limited resources, infrastructural barriers, and lack of data to help you modify your models for local implementation. You will be able to engage the solutions and address successful deep-learning tasks in Nepal by the end of the course.

Teaching Methodology

• Handful of assignments, tutorials, and lab tests of each chapter.
• Periodic feedback from the Trainer and Trainee and the required changes as necessary.
• Each trainee needs to develop a demo application on their own, taking assistance from the trainer whenever required
  
  

Prerequisites

• Good command of the English language.
• Good knowledge of Basic Computer Skills.
• Basic Knowledge of any programming language or Python would be a plus.
• Understanding of Software and Software installation.