Deep Learning Certification – Ai and Machine Learning

Deep Learning Training in Malaysia

Deep learning course in Malaysia empowers you with the knowledge and skill to develop a neuronal network, which is the replica of the human brain. As deep learning is the subsets of Ai and ML, thus, deep learning certification comprises the topics such as artificial intelligence and machine learning. Deep learning is completely a performance-based online training that includes creating a complex neuronal network using big data and data science technology. Enroll & Get Certified now!

✔ Course Duration : 32 hrs

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

✔ Certification Pass : Guaranteed

DEEP LEARNING COURSE MALAYSIA

Deep learning training malaysia is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course malaysia 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.Deep Learning Training Malaysia It is one of the most popular software platforms used for deep learning training malaysia and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning course malaysia 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.Deep learning Course Malaysia With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. Deep learning course malaysia according to payscale.com, the median salary for engineers with deep learning training malaysia skills tops $120,000 per year.

What you will learn in deep learning training

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 of deep learning course

Software engineers
Data scientists
Data analysts
Statisticians with an interest in deep learning

CURRICULUM of deep learning certification

Introduction to deep learning

What is a neural network?
Supervised Learning with Neural Networks
Why is Deep Learning taking off?
Introduction to deep learning

Neural Networks Basics

Binary Classification
Logistic Regression
Logistic Regression Cost Function
Gradient Descent
Derivatives
Computation graph
Derivatives with a Computation Graph
Logistic Regression Gradient Descent

Vectorization

Vectorizing Logistic Regression
Vectorizing Logistic Regression's Gradient Output
Broadcasting in Python
A note on python/numpy vectors
Quick tour of Jupyter/iPython Notebooks
Explanation of logistic regression cost function (optional)

Shallow neural networks

Neural Networks Overview
Deep Learning Honor Code
Logistic Regression with a Neural Network mindset
Neural Network Representation
Computing a Neural Network's Output
Vectorizing across multiple examples
Explanation for Vectorized Implementation
Activation functions
Why do you need non-linear activation functions?
Derivatives of activation functions
Gradient descent for Neural Networks
Backpropagation intuition (optional)
Random Initialization

Deep Neural Networks

Deep L-layer neural network
Forward Propagation in a Deep Network
Getting your matrix dimensions right
Why deep representations?
Building blocks of deep neural networks
Forward and Backward Propagation
Parameters vs. Hyperparameters
What does this have to do with the brain?
Deep Neural Network – Application

Key concepts on Deep Neural Networks

Building your Deep Neural Network: Step by Step
Deep Neural Network - Application

Foundations of Convolutional Neural Networks

Computer Vision
Edge Detection
Padding
Strided Convolutions
Convolutions Over Volume
One Layer of a Convolutional Network
Simple Convolutional Network Example
Pooling Layers
Why Convolutions?

Deep convolutional models: case studies

Object detection

Object Localization
Landmark Detection
Object Detection
Convolutional Implementation of Sliding Windows
Bounding Box Predictions
Intersection Over Union
Non-max Suppression
Anchor Boxes
YOLO Algorithm

Special applications: Face recognition & Neural style transfer

What is face recognition?
Siamese Network
Triplet Loss
Face Verification and Binary Classification
What is neural style transfer?
What are deep ConvNets learning?
Cost Function
Content Cost Function
Style Cost Function
1D and 3D Generalizations
Face Recognition for the Happy House

Recurrent Neural Networks

Why sequence models
Notation
Recurrent Neural Network Model
Back propagation through time
Different types of RNNs
Language model and sequence generation
Sampling novel sequences
Vanishing gradients with RNNs
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
Bidirectional RNN
Deep RNNs
Building a recurrent neural network - step by step
Dinosaur Island - Character-Level Language Modeling
Other-Jazz improvisation with LSTM

Natural Language Processing & Word Embeddings

Word Representation
Using word embeddings
Properties of word embeddings
Embedding matrix
Learning word embeddings
Word2Vec
Negative Sampling
GloVe word vectors
Sentiment Classification
Debiasing word embeddings
Operations on word vectors – Debiasing

Sequence models & Attention mechanism

Basic Models
Picking the most likely sentence
Beam Search
Refinements to Beam Search
Error analysis in beam search
Bleu Score (optional)
Attention Model Intuition
Attention Model
Speech recognition
Trigger Word Detection
Neural Machine Translation with Attention
Trigger word detection

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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