Deep Learning with TensorFlow
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. It is intersection of statistics, artificial intelligence, and data to build accurate models. TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible.
How it works
A deep learning model is designed to continually analyse data with a logic structure like how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN).
What you will learn from this course
This course will offer you an opportunity to explore various complex algorithms for deep learning. You will also learn how to train model to derive new features to make sense of deeper layers of data. Using TensorFlow, you will learn how to train model in supervise and unsupervised category.
Introduction to Deep learning
AI and Deep learning
Advantage of DL
Deep Learning Primitives
Deep Learning Architecture
The Neural viewpoint
The Representation Viewpoint
Introduction of Tensors
Installation of Tensors
Scalars, Vectors, and Matrices
Initializing Constant Tensors
Basic Computation using TensorFlow
Sampling Random Tensors
Tensor Addition and Scaling
Tensor Shape Manipulation
Logistic Regression Model Building and Training
Introduction to Neural Network
Basic Neural Network
Single Hidden Layer Model
Multiple Hidden Layer Model
Input, Output, Hidden Layers
Details of Activation Functions: Sigmoid Function Hyperbolic Tangent
Selection of Right Activation Functions
Network learning technique
Linear and Logistic Regression with TensorFlow
Overview of Linear and Logistic Regression
Automatic Differentiation Systems
Learning with TensorFlow
Training Linear and Logistic Regression model
Evaluating Model Accuracy
Convolutional Neural Networks
Visual Cortex Architecture
Stacking Multiple Feature Maps
Fully Connected Layer
MNIST digit classification example
Recurrent Neural Networks
Input and Output Sequences
Basic RNNs in TensorFlow
Static Unrolling through Time
Dynamic Unrolling through Time
Handling Variable Length Input/Output Sequence
Distributing a Deep RNN Across Multiple GPUs
The Difficulty of Training over many Time Steps
Introduction to OpenAI Gym
Neural Network Policies
The Credit Assignment Problem
Markov Decision Process
Temporal Difference Learning and Q-Learning
Approximate Q-Learning and Deep Q-Learning
Basic understanding of linear algebra , calculus and probability are must for really understanding deep learning . It is expected that one has some knowledge or experience in basic Python programming skills with the capability to work effectively with data structures . Understanding how to frame a machine learning problem, including how data is represented will be an added advantage.
Who can attend
Anyone who has coding experience with an engineering background or relevant knowledge in mathematics and computer science can take this session to get understanding of Deep learning.
Duration & Timings :
Duration – 40 Hours.
Training Type: Online Live Interactive Session.
Access to Class Recordings.
Weekday Session – Mon – Thu 8:30 PM – 10:30 PM EST– 4 Weeks. January 21, 2019.
Weekend Session – Sat & Sun 9:30 AM to 12:30 PM (EST) – 5 Weeks. February 16, 2019.