Deep learning

DEEP LEARNING

The following topics will be covered in our

Deep Learning Online Training:

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Deep Learning with TensorFlow â&#x2013;Ş 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.

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How it works â&#x2013;Ş A deep learning model is designed to continually analyze 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).

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What you will learn from this course

â&#x20AC;˘ 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.

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Introduction to Deep learning • AI and Deep learning • Advantage of Deep learning • Deep Learning Primitives • Deep Learning Architecture • The Neural viewpoint • The Representation Viewpoint

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TensorFlow Fundamentals • Introduction of Tensors

• Tensor Addition and Scaling

• Installation of Tensors

• Matrix Operation

• Scalars, Vectors, and Matrices

• Tensor Shape Manipulation

• Matrix Mathematics

• Tensor Types

• Initializing Constant Tensors

• TensorFlow Graphs

• Basic Computation using TensorFlow

• TensorFlow Sessions

• Sampling Random Tensors

• Logistic Regression Model Building and

Training

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Introduction to Neural Network • Basic Neural Network

• Selection of Right Activation Functions

• The Neurons

• Network learning technique

• Single Hidden Layer Model

• Weight initialization

• Multiple Hidden Layer Model

• Forward Propagation

• Input, Output, Hidden Layers

• Backpropagation

• Details of Activation Functions: Sigmoid • Optimization Algorithms Function Hyperbolic Tangent

• Regularization

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Linear and Logistic Regression with TensorFlow • Overview of Linear and Logistic Regression • Loss Functions • Gradient Descent • Automatic Differentiation Systems

• Learning with TensorFlow • Training Linear and Logistic Regression model • Evaluating Model Accuracy

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Convolutional Neural Networks • Visual Cortex Architecture • Convolutional Layer

• Filters • Stacking Multiple Feature Maps • TensorFlow Implementation • Pooling/Subsampling • Fully Connected Layer

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Recurrent Neural Networks •Recurrent Neurons

• Training RNNs

•Memory cells

• Creative RNNs

•Input and Output Sequences

• Deep RNNs

•Basic RNNs in TensorFlow

• Distributing a Deep RNN Across Multiple

•Static Unrolling through Time •Dynamic Unrolling through Time •Handling Variable Length Input/output

GPUs • The Difficulty of Training over many Time Steps

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Reinforcement Learning • Policy Search

• Markov Decision Process

• Introduction to OpenAI Gym

• Temporal Difference Learning and Q-

• Neural Network Policies • The Credit Assignment Problem • Policy Gradients

Learning • Approximate Q-Learning and Deep QLearning

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Prerequisites :

â&#x20AC;˘ 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.

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Who can attend â&#x2013;Ş 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.

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