Training & Internship - Deep Learning with TensorFlow
TensorFlow is a powerful open-source software library for machine learning developed by researchers at Google. It has many pre-built functions to ease the task of building different neural networks. TensorFlow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.
Upcoming Batch date
Please contact us from https://www.xen.ai/contact/
Timings for online class
3 hours Online class sessions on Saturdays & Sundays [Class timings: 2 pm - 5 pm]
Rs. 19,980/- or Rs. 6,660/- x 3 Installments
Please register using the form at the end of this page.
IT Professionals:- Those who would like to switch their field or upgrade their skills in ML and Data Science.
Students :- Those who aspire to form their career in the field of AI
Anyone Who love Data Science and AI
Proficiency in Python:
Knowledge in Machine Learning:
Deep Learning in Python
Overview of important python packages for Deep Learning
What is Tensor Flow?
Tensor Flow code-basics
Constants, Placeholders, Variables
Tensorflow Basic Operations
Linear Regression with Tensor Flow
Logistic Regression with Tensor Flow
K Nearest Neighbor algorithm with Tensor Flow
K-Means classifier with Tensor Flow
Random Forest classifier with Tensor Flow
Neural Networks using Tensor Flow
Quick recap of Neural Networks
Activation Functions, hidden layers, hidden units
Illustrate & Training a Perceptron
Important Parameters of Perceptron
Understand limitations of A Single Layer Perceptron
Illustrate Multi-Layer Perceptron
Back-propagation – Learning Algorithm
Understand Back-propagation – Using Neural Network Example
Deep Learning Networks
What is Deep Learning Networks?
Why Deep Learning Networks?
How Deep Learning Works?
Working of Deep Network
Training using Backpropagation
Variants of Gradient Descent
Types of Deep Networks
Feed forward Neural Networks (FNN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Generative Adversal Neural Networks (GAN)
Restrict Boltzman Machine (RBM)
Convolutional Neural Networks (CNN)
Introduction to Convolutional Neural Networks
Architecture of a Convolutional Neural Network
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN
Transfer Learning and Fine-tuning Convolutional Neural Networks
Recurrent Neural Network (RNN)
Intro to RNN Model
Application use cases of RNN
Training RNNs with Backpropagation
Long Short-Term Memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model
Restricted Boltzmann Machine (RBM)
What is Restricted Boltzmann Machine?
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders & Applications
Deep Learning with Keras
How to compose Models in Keras
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras
Intuitively building networks with Keras
Natural Language Processing (NLP)
Introduction to NLP
Case studies on NLP
Deep Learning with TensorFlow project using Industrial Application
- NLP Chatbot
- Text Classification
- Face & Image Recognition
- Object Detection
- Deep Fake
- Stock Price Prediction
- Fraud Detection
- Recommender System
Key Takeaways from the Deep Learning course:
- Cutting edge coding skills for Deep Learning
- Assignments and Review every Week.
- Hands on Industrial project use case at the end of the course.
- Certification after completing all the Assignment and Internship.
- Placement and Internship Opportunities.
If you would like to join this program please register below: