Deep Learning

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Deep Learning

Welcome to TensorFlow's Complete Guide to Deep Learning with Python!

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  • (0 Reviews)
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  • ৳ 10,000
  • ৳ 15,000


What you will learn

  • Insights behind artificial neural networks.
  • Insights behind machine learning.
  • Insights behind regression and classification.
  • Application of artificial neural networks in practice.

Course Content

15 sections • 0 lectures •

Requirements

  • High school math, basic Python programming knowledge and some basic knowledge of machine learning.

Description

This course will guide you on how to use Google's TensorFlow framework to build artificial neural networks for deep learning. This course aims to provide you with an easy-to-understand guide to the intricacies of Google's TensorFlow framework to make it easy to understand, Deep learning techniques.

This course is designed to balance theory and practical implementation with complete Jupyter Notebook code guides and easy reference slides and notes.

This course covers a variety of topics, including

  • Artificial Intelligence and machine learning Basics
  • Regression and classifications basics
  • Neural Network Basics
  • Tensor Flow Basics
  • Artificial Neural Networks
  • Densely Connected Networks
  • Convolutional Neural Networks
  • Data augmentation
  • Recurrent Neural Networks
  • AutoEncoders
  • And much more!

There are many deep learning frameworks out there, so why use TensorFlow?

TensorFlow is an open-source software library for numerical computations using data flow graphs. The nodes of the graph represent mathematical operations, while the edges of the graph represent the multidimensional data arrays (tensors) communicated between them. Flexible architecture lets you deploy computations on one or more CPUs or GPUs on desktops, servers, or mobile devices with a single API.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's machine intelligence research organization to conduct machine learning and deep neural network research, but the system is general enough to be applicable to a wide variety of other domains.

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About the Instructor

instructor
About the Instructor

SKILLS:

Expert: Machine Learning, Deep Learning, and Data Science

Programming Language:

  • C, C++, C#
  • Java
  • Python
  • PHP
  • Swift
  • Knowledge of HTML CSS and JavaScript
  • Natural Language Processing (NLP)
  • TensorFlow, PyTorch

Tools:

  • PyCharm
  • Anaconda3
  • Matlab
  • Unity
  • Microsoft Visual Studio
  • Android Studio
  • Tableau
  • Git

PUBLICATIONS:

  1. Masum, S.M., Walid M., Zeyad M., Galib, S.M. & Nesa, M. (2021). Analysis of Machine Learning
    Strategies for Prediction of Prosperity Tend of Undergraduate Admission Test in the Context of
    Bangladesh. In Engineering Science and Technology. [under review]
  2. Nesa, M., & Rani, T. (2020, October). Prediction of Juvenile Crime because of Drug Addiction &
    Prevention Strategies with Data & Analytics. In 2020 International Conference on Data Science and
    Machine Learning (DSML). [accepted]

PROJECTS:

  • Hospital Management System
  • Zigzag Replica
  • Snake Game

LANGUAGE:

Bengali

English Proficiency: IELTS Score: 6; Listening: 6.5, Reading: 6, Speaking: 5.5, Writing: 6.5