Deep Learning Specialty Training

• Gain a comprehensive understanding of various deep learning models, including Neural Networks, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
• Learn to implement deep learning algorithms, understand neural networks, and traverse layers of data abstraction to understand the architecture of different types of deep networks.
• Develop a solid understanding of Back propagation - a fundamental concept in deep learning used for training neural networks.
• Learn to build, train, and deploy various types of deep architectures including CNNs, RNNs, auto encoders, and Generative Adversarial Networks (GANs).
• Understand the role of different activation functions, loss functions, optimization algorithms, regularization methods, and understand how to choose the appropriate function or method for a given task.
• Learn how to use GPUs for training deep learning models more efficiently. Understand the ethical considerations, privacy issues, and societal implications of using deep learning technologies.

• Recorded Videos After Training
• Digital Learning Material
• Course Completion Certificate
• 24x7 After Training Support

• Machine Learning Engineers and Data Scientists seeking to enhance their knowledge of deep learning and apply it in their professional field.
• Software Engineers wanting to understand and apply deep learning models in creating or enhancing applications.
• Research Scholars and Academicians who are researching or teaching in fields where deep learning models could be applied.
• Professionals from other disciplines who are keen on switching their careers into AI and Machine Learning.
• Students studying computer science or related fields who wish to gain expertise in deep learning.
• Tech enthusiasts and hobbyists who have a keen interest in AI, machine learning, and deep learning technologies.

• Strong foundation in mathematics: Proficiency in linear algebra, calculus, probability, and statistics is essential for understanding deep learning concepts and algorithms. Programming skills: Proficiency in at least one programming language, preferably Python, which is the most used language in the deep learning community.
• Basic understanding of machine learning: Familiarity with key machine learning concepts, algorithms, and techniques, such as supervised and unsupervised learning, feature engineering, and model evaluation.
• Knowledge of neural networks: Understanding the basic concepts and architecture of neural networks, including feedforward networks, backpropagation, and gradient descent.
• Familiarity with deep learning frameworks: Experience with popular deep learning libraries and frameworks, such as TensorFlow, Keras, or PyTorch, is highly beneficial.
• Hands-on experience: Practical experience applying deep learning models to real-world problems, such as image classification, natural language processing, or reinforcement learning.

• Multisoft Systems provides a globally recognized training certificate after successful completion of Deep Learning Specialty certification course.

Module 1: Introduction to Deep Learning

• Introduction to deep learning trends and applications
• Examples of deep learning applications

Module 2: Neural Network Basics

• Machine learning problem setup and neural network mindset
• Vectorization for efficient computation

Module 3: Shallow Neural Network

• Building a neural network with one hidden layer
• Understanding forward propagation and backpropagation

Module 4: Deep Neural Network

• Computation in deep learning
• Building and training deep neural networks for computer vision tasks

Module 5: Practical Aspects of Deep Learning

• Initialization methods for deep neural networks
• Regularization techniques to prevent overfitting

Module 6: Optimization Algorithms

• Advanced optimization techniques for neural networks
• Random minibatching and learning rate decay

Module 7: Hyperparameter Tuning, Batch Normalization, Frameworks

• Introduction to the TensorFlow framework
• Training neural networks on TensorFlow datasets

Module 8: ML Strategy

• Strategic guidelines for setting goals and managing ML production workflow
• Error analysis procedures

Module 9: Foundations of Convolutional Neural Networks

• Understanding pooling and convolutional layers
• Building deep CNNs for image classification

Module 10: Deep Convolutional Models: Case Studies

• Exploring advanced tricks and methods in deep CNNs
• Applying transfer learning to pretrained models

Module 11: Object Detection

• Using CNNs for object detection tasks

Module 12: Face Recognition & Neural Style Transfer

• Applying CNNs for face recognition tasks
• Implementing neural style transfer for art generation

Module 13: Recurrent Neural Networks

• Introduction to recurrent neural networks (RNNs)
• Variants of RNNs for sequential data modeling

Module 14: Natural Language Processing & Word Embeddings

• NLP applications with deep learning models
• Word embeddings for text analysis

Module 15: Sequence Models & Attention Mechanism

• Enhancing sequence models with attention mechanisms
• Speech recognition and audio data processing

Module 16: Transformer Network

• Understanding the functioning of transformer networks