Deep Learning Training in Noida | Deep Learning Course with TensorFlow

Deep learning Training Noida

Deep learning Training Noida | Deep learning Master Course Noida| Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence. In this course we will start with deep learning introduction and you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning. Mildaintrainings provides The Best Deep Learning Course by Industry Experts. Enroll & Get Certified now!

✔ Course Duration : 32 hrs

✔ Training Options : Live Online / Self-Paced / Classroom

✔ Certification Pass : Guaranteed

Deep learning Training Noida

Deep learning is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course is designed to help you master deep learning techniques and build deep learning models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research. It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.

What you will Learn

Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
Build deep learning models in TensorFlow and interpret the results
Understand the language and fundamental concepts of artificial neural networks
Troubleshoot and improve deep learning models
Build your own deep learning project
Differentiate between machine learning, deep learning and artificial intelligence

PREREQUISITES

Bring the requirements of organization functions and you can meet the targets of an organzition.

CURRICULUM

Learning Objectives:

Machine Learning Primer
Machine Learning core concepts, scalable algorithms, project workflow
Objective Functions and Regularization
Understanding Objective Function of ML Algorithms
Metrics, Evaluation Methods and Optimizers
Popular Metrics in Detail: R2 Score, RMSE, Cross Entropy, Precision, Recall, F1 Score, ROC AUC, SGD, ADAM
Artificial Neural Network
ANN in detail, Forward Pass and Back Propagation
Machine Learning Vs Deep Learning
Core difference b/w ML and DL from implementation perspective

Learning Objectives:

Python Programming Primer
Installing Python, Programming Basics, Native Data types
Class, Inheritance and Magic Functions
Python Classes, Inheritance Concepts, Magic Functions
Special Functions in Python
Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key functions
Decorators and Special Functions
Decorators implementation with class
Context Manager ‘with’ in Python
Context Manager Application
Exception Handling
Try and Catch block
Python Package Management
Bundling and export python packages

Learning Objectives:

TensorFlow 2.0 Basics
TensorFlow core concepts, Tensors, core APIs
Concrete Functions, Datatypes, Control Statements
Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas
Autograph eager execution
tf.function autograph implementation
Sessions vs tf.function
Keras (TensorFlow 2.0 Built-in API) Overview
Sequential Models, configuring layers, loading data, train and test, complex models, call backs, save and restore Neural Network weights
Building Neural Networks in Keras
Building Neural networks from scratch in Keras
Implementing RNN, CNN in Keras
Building Recurrent Neural Networks for sequence data and Convolution Neural Networks for Image Classification

Learning Objectives:

Linear Algebra
Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
Calculus – Derivatives: Calculus essentials, Derivatives and Partial Derivatives, Chain Rule, Derivatives of special functions
Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross Entropy, Quadratic Loss Functions

Learning Objectives:

Deep Learning Network Concepts
Core concepts of Deep Learning Networks
Deep Dive into Activation Functions
Relu, Sigmoid, Tanh, SoftMax, Linear
Building simple Deep Learning Network
Simple DL network from starch
Tuning Deep Learning Network
Tuning Deep Learning Network Parameters for optimal performance, Stopping Criteria
Visualizing Training using TensorBoard
Visualizing Deep Learning Network using TensorBoard

Learning Objectives:

Deep Learning Architectures
Popular Deep learning Architectures – CNN, RNN, LSTM RNN, GRU RNN Introduction
Deep Dive into Convolutional Neural Network
Core Concepts of Convolutional Neural Network, Feature Maps, Relu Activation, Max Pooling
CNN Application – Image Classification
Image Classification implementation with CNN TensorFlow 2.0 (Keras)
Recurrent Neural Networks (RNN) Basics
RNN Architecture, BPTT Backprop through time, Mathematics of RNN
RNN, LTSM RNN and GRU RNN
Vanishing Gradient and exploding Gradient problem, LTSM architecture, GRU Architecture.
LSTM RNN implementation in TensorFlow
LSTM RNN project.

Learning Objectives:

Big Data Platforms
Importing Big Data
PySpark functions for importing data from various sources and other big data frameworks
Machine Learning with PySpark
Implementing scalable ML models with PySpark

Learning Objectives:

Image Classification
Image Classification with CIFAR-10 Dataset
Human Face Detection
Traffic Sign Detection
Human Activity Detection
20BN-something-something Dataset V2
Image Caption Generation

FAQs

You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

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