Deep Learning: Hands On using TensorFlow & Keras
- Created By raju2006
- Last Updated December 12th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Unlock the potential of Artificial Intelligence with our comprehensive Deep Learning course, featuring hands-on training using TensorFlow and Keras. As technology advances, mastering Deep Learning is essential to stay at the forefront of AI and machine learning. Deep Learning, a powerful subset of Machine Learning, excels in accuracy, especially with extensive data.
Our course caters to intermediate to advanced professionals, addressing the surging demand for skilled Deep Learning Engineers across diverse industries. Dive into the world of nested hierarchies, where concepts are intricately linked, and abstract ideas are derived from simpler ones. This certification training empowers you to harness the supremacy of Deep Learning, enabling you to predict future outcomes based on past data patterns. Join us on this journey towards AI excellence!
Course Code/Duration:
BDT190 / 3 Days
Learning Objectives:
After this course, you will be able to:
- Grasp the fundamentals of Artificial Neural Networks (ANNs).
- Explore Convolutional Neural Networks (CNNs) from basics to advanced concepts.
- Understand when and why to use Recurrent Neural Networks (RNNs).
- Apply CNNs in real-world scenarios.
- Gain practical experience with RNNs in a real-life context.
- Get hands-on training in TensorFlow and Keras.
- Build and fine-tune CNN models using TensorFlow.
- Master the applications of Long Short-Term Memory (LSTM) networks.
- Learn real-time object detection using YOLO (You Only Look Once).
- Basic programming knowledge and basic understanding of machine learning concept.
- Anyone like any stream programmer, Analyst, data engineer, want to started their career or know more about Machine Learning and Deep Learning.
- Anyone like any stream programmer, Analyst, data engineer, want to started their career or know more about Machine Learning and Deep Learning.
Course Outline:
1. Introduction and Basic Knowledge
- Overview of Machine Learning
- Different type of ML
- IDE (Anaconda) Installation and Intro.
- Simple Program of basic ML and Hands On.
2. Introduction of Neural Networks
- Introducing Perceptrons
- Step Function
- Updating the weights
- Hidden Layers
- Activation functions
- Loss functions
- Gradient descent
- Back propagation
- Vanishing gradient problem and ReLU
- Understanding the intuition behind neural networks
3. Introduction of TensorFlow and Keras
- Why TensorFlow and Keras
- Difference in Tensorflow and keras
- Sample Code and Hands on Tensorflow and Keras
4. Introduction to Convolutional Neural Networks
- Convolutional layers
- Pooling layers
- Kernel
- Stride
- Padding
- Pooling
- Flatten
- Fully connected layers
5. Building CNN using TensorFlow
- Image recognition
- Hyperparameter tuning
- Image augmentation
- Visualize Modes TensorBoard
6. Recurrent Neural Network
- Why RNN and where to use
- Basic concept and architecture of RNN
- Sample Code and Hands on
7. LSTM (Long Short Term Memory)
- Why LSTM and where to use
- Basic difference among ML, DL, RNN, LSTM
- Basic concept and architecture of RNN
8. RNN: Building Code
- Pre-processing text
- Word Embeddings
- Natural Language Processing
9. Real Time Object Detection
- YOLO (You Only Look Once) basic and Installation
- Hands on YOLO
- Object Detection
Training material provided:
Yes (Digital format)
