3 Days Live Virtual Training on AI and Deep Learning with Python
Training TypeLive Training
About the Course
The AI and Deep Learning with Python Certification course enables you to take your lastest skills like AI and Deep Learning into a variety of companies, helping them to apply these techniques on the data and make more informed business decisions. The course covers predictive analytics techniques with the Python language. You will learn about various Python packages like Tensorflow and Keras. This will give you a deep understanding on algorithms like Artificial Neural Networks, Convolutional Neural Networks and Recurrent neural networks.
Install Python, Jupyter Notebook,and learn about the various Python packages
Gain an in-depth understanding of data structure used in Python and learn to import/export data in Python
Define, understand and use the various functions in Python
Learn Python packages like Tensorflow and Keras
Learn indepth knowledge on AI and Deep learning algorithms like ANN, CNN and RNN and its various use cases.
Who is the Target Audience?
This course is meant for all those students and professionals who are interested in using the Python's powerful ecosystem
There are no prerequisites
|23 Jun 2021||Wed - Fri (3 Day)||08:00 AM - 01:00 PM (PT)|
History of Neural networks and Deep Learning.
How Biological Neurons work?
Growth of biological neural networks
Diagrammatic representation: Logistic Regression and Perceptron
Multi-Layered Perceptron (MLP).
Training a single-neuron model.
Training an MLP: Chain Rule
Training an MLP:Memoization
Vanishing Gradient problem.
Decision surfaces: Playground
Deep Multi-layer perceptrons:1980s to 2010s
Dropout layers & Regularization.
Rectified Linear Units (ReLU).
Optimizers:Hill-descent analogy in 2D
Optimizers:Hill descent in 3D and contours.
Batch SGD with momentum.
Nesterov Accelerated Gradient (NAG)
Optimizers : Adadelta andRMSProp
Which algorithm to choose when?
Gradient Checking and clipping
Softmax and Cross-entropy for multi-class classification.
How to train a Deep MLP?
Word2Vec :Algorithmic Optimizations.
Tensorflow and Keras overview
GPU vs CPU for Deep Learning
Online documentation and tutorials
Softmax Classifier on MNIST dataset
Model 1: Sigmoid activation
Model 2: ReLU activation
Model 3: Batch Normalization
Model 4 : Dropout
Biological inspiration: Visual Cortex
Convolution:Edge Detection on images.
Convolution:Padding and strides
Convolution over RGB images.
CNN Training: Optimization
Receptive Fields and Effective Receptive Fields
Convolution Layers in Keras
What is Transfer learning.
Recurrent Neural Network
Training RNNs: Backprop
Types of RNNs
Need for LSTM/GRU