Projects
Assignments
Overview
In this Deep Learning course with TensorFlow training, participants will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, they will be able to build deep learning models, interpret results, and build your own deep learning project.
Objectives
At the end of Deep Learning with TensorFlow training course, participants will be able to
Prerequisites
Participants in this training should have familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.
Course Outline
AI and Deep Learning Introductionedforce2021-06-30T17:22:43+05:30
- What is AI and Deep Learning
- Brief History of AI
- Recap: SL, UL and RL
- Deep Learning: Successes Last Decade
- Demo and Discussion: Self-Driving Car Object Detection
- Applications of Deep Learning
- Challenges of Deep Learning
- Demo and Discussion: Sentiment Analysis Using LSTM
- Full Cycle of a Deep Learning Project
Artificial Neural Networkedforce2021-06-30T17:27:19+05:30
- Biological Neuron Vs Perceptron
- Shallow Neural Network
- Training a Perceptron
- Backpropagation
- Role of Activation Functions and Backpropagation
- Optimization
- Regularization
- Dropout layer
Deep Neural Network & Toolsedforce2021-06-30T17:27:42+05:30
- Deep Neural Network: Why and Applications
- Designing a Deep Neural Network
- How to Choose Your Loss Function?
- Tools for Deep Learning Models
- Keras and its Elements
- Tensorflow and Its Ecosystem
- TFlearn
- Pytorch and its Elements
Deep Neural Net optimization, tuning, interpretabilityedforce2021-06-30T17:28:10+05:30
- Optimization Algorithms
- SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
- Batch Normalization
- Exploding and Vanishing Gradients
- Hyperparameter Tuning
- Interpretability
Convolutional Neural Netedforce2021-06-30T17:28:24+05:30
- Success and History
- CNN Network Design and Architecture
- Deep Convolutional Models
Recurrent Neural Networksedforce2021-06-30T17:29:07+05:30
- Sequence Data
- Sense of Time
- RNN Introduction
- LSTM (Retail Sales Dataset Kaggle)
- Word Embedding and LSTM
- GRUs
- LSTM vs GRUs
Autoencodersedforce2021-06-30T17:29:35+05:30
- Introduction to Autoencoders
- Applications of Autoencoders
- Autoencoder for Anomaly Detection
