AI Powered Deep Learning: From Fundamentals to Advanced Applications
Learning Path | 1 Course Series

Synopsis

Description

Deep Learning is a new part of Machine Learning, which has been introduced with the objective of moving Machine Learning closer to Artificial Intelligence. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this training we are going to learn and apply concepts of deep learning with live projects.

1. Introduction:

Begin your journey into deep learning with a comprehensive introduction to the field. Understand the core principles of machine learning and popular methods used in the industry. Explore the concept of deep learning and its wide-ranging applications across domains such as image recognition, natural language processing, and recommendation systems. Receive valuable recommendations on how to approach and navigate the deep learning landscape effectively.

2. Deep Learning Basics:

Build a solid foundation in deep learning by delving into its fundamental concepts. Explore the workings of a neural network, including the perception and the Universal Approximations Theorem. Dive deep into the architecture of deep neural networks and understand how they enable the learning of complex patterns from data.

3. Getting Started:

Equip yourself with the necessary tools and environment to kickstart your deep learning journey. Learn where and how to write code, whether it's in a Jupyter Notebook, Google Colab, or using PyTorch. Understand the fundamental building blocks of deep learning, including tensors and gradients, and apply your knowledge with practical examples using the MNIST dataset.

4. Image Classifier:

Step into the realm of image classification with an in-depth exploration of transfer learning and convolutional neural networks (CNNs). Learn how to preprocess and transform datasets, visualize data, define and train your model, and evaluate its performance. Gain practical experience in building image classifiers that can identify objects and patterns within images.

5. Text Classifier:

Discover the world of text classification using deep learning techniques. Explore how convolutional neural networks (CNNs) can be applied to analyze and categorize text data. Learn how to preprocess text data, build and train your model, define loss functions, and assess the accuracy of your text classifier.

6. Text Generation:

Uncover the fascinating field of text generation with transformers. Gain insights into transformer architectures and their role in generating coherent and contextually relevant text. Dive deep into word generation and text generation techniques, exploring various transformer architectures and their applications.

7. Text Translation:

Delve into the intricacies of text translation using attention mechanisms. Learn about encoder-decoder architectures and how attention mechanisms improve translation quality. Gain practical experience in training models to translate text from one language to another.

8. Prediction on Tabular Data:

Master the art of making predictions on tabular data using deep learning methods. Learn how to preprocess tabular data, define and train your models, and evaluate their performance. Explore various techniques for predicting outcomes based on structured data.

9. Collaborative Filtering:

Explore collaborative filtering techniques for building recommendation systems. Understand how collaborative filtering algorithms leverage user behavior data to generate personalized recommendations. Learn about different recommendation approaches and their applications in real-world scenarios.

Throughout the course, you will not only gain theoretical knowledge but also practical hands-on experience in implementing deep learning techniques across a wide range of applications. Whether you're interested in image recognition, natural language processing, or recommendation systems, this course will equip you with the skills and tools necessary to excel in the field of deep learning.