10 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun | |
16 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun | |
17 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun | |
23 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun |
Course Price At
10 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun | |
16 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun | |
17 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun | |
23 Nov 2024 | 24 | 06:00 PM - 09:00 PM | Sat, Sun |
Course Price At
Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You’ll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.
Course Price At
Deep Learning A-Z: Hands-On Artificial Neural Networks online training offered by Multisoft Virtual Academy helps the candidates in gaining complete acquaintance on the Deep Learning essentials so that they would be able to develop an understanding on the intuition behind Artificial Neural Networks. Deep Learning is an essential part of Artificial Intelligence learning, so to provide complete acquaintance on the Deep Learning, Multisoft Virtual Academy offers acquaintance on Deep Learning A-Z:
1 Robust Structure: To understand and navigate the complexity of the structure, the training focuses on two fundamental branches under Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning.
2 Intuition Tutorials: The candidates get to know the concepts behind Deep Learning algorithms and get hands-on coding exercises as well, so that they would get to understand the techniques of an instinctive level.
3 Exciting Projects: this section helps the candidates in enhancing their insight on major real time dataset issues:
4 Hands-on Coding: Helps the candidates in understanding the Deep Learning A-Z code, this explains to modify the code step-by-step and insert in the dataset.
5 In-Course Support: This helps in getting acquainted with the tools that help in evaluating the performance of the models, improving the models with effective Parameter Tuning and insight on technique and K-Fold Cross Validation.
1. Artificial Neural Networks
2. Convolutional Neural Networks
The candidates required to have good knowledge of high school level mathematics.