Deep Learning is a new and fast growing important component of Artificial Intelligence.
Deep Learning is also a form of machine learning but with enhanced capabilities . While Machine Learning algorithms usually need some guidance ,the Deep learning techniques need no guidance and can work completely based on the data to provide more accurate outputs.
Hence Training in Deep Learning is becoming essential for Artificial Intelligence Enthusiasts.
Deep Learning models are based on the functioning of Human Brains and consist of artificial neural network (ANN) which are fashioned on the biological neural network of the human brain. ANN is a complex structure of layered multi- algorithms which analyzes data in step wise logic structure and patterns similar to how a human brain analyzes patterns and makes conclusion. However the scope of “Human Error” in analysis becomes minimal in case of Deep Learning and hence its performance can even surpass human logic and capability boundaries.
Hence deep learning models can perform extremely well but they need high-end algorithms and significantly high volume of data to provide accurate results.
Deep Learning is a complex and advanced components of Artificial Intelligence and hence to understand and gain mastery in it , you need to have expertise in machine learning algorithms.
A simple Example of difference between Machine Learning and Deep Learning
If you wish to classify pictures of dogs and cats – Machine learning algorithms can do that quickly and accurately once you have provided some initial features related to Dogs and Cats to the machine , while Deep Learning can automatically discover the features that can classify Dogs from Cats and classify all pictures based on its own derived classification.
Topics covered in Ambeone’s Deep Learning Training
Following are the main topics covered in our Deep Learning Training programs
- Introduction to Neural Networks
- Objective functions
- Activation Functions
- Cost Functions
- Gradient Descent
- Building a neural network with NumPy
- TensorFlows-an open source framework for creating Deep Learning Models
- Keras – A Neural network Library
- Deep Neural Networks
- Deep Learning Over Fitting
- Deep Learning Initialization
- Stochastic Gradient Descent and learning rate schedule
- Introduction to Convolution Neural Networks for analyzing visual imagery
- Introduction to Recurrent Neural Networks for speech recognition and natural Language processing
- Artificial Intelligence: Reinforcement Learning
To pursue deep learning training one needs to first understand and be familiar with the foundation of Statistics , Python and machine learning techniques
We at Ambeone offer a highly structured comprehensive training that covers all the pre-requisites of training in Deep Learning through our Associate program.
Once you have mastered all the pre-requisite components like Statistics, R and Python and Machine Learning , you can start training in Deep Learning .
Understanding the concepts of Artificial Neural Networks and other Deep Learning components will equip you to start developing your Artificial Intelligence applications and models.
We offer Hands-on & In-Person Training on all components of Deep Learning Programs
Prerequisite for this Course
- Participants in this course must have completed our course on Machine Learning.
- Or they must demonstrate good understanding of the following topics by passing an examination prior to course registration.
- Fundamentals of Statistics. This course will be heavily using Statistical concepts to build AI models and it is expected participants have a robust understanding of Statistics.
- Programming in Python. This course will be mainly using Python as the programming language. It is expected that participants have experience working with Python, especially related to Data Manipulation, Data Visualization & Basic Machine Learning.
- Machine Learning. This course will be building on topics covered in our Machine Learning Module and it is expects participants have sound understanding of topics covered in that course.
- We offer Discount Packages for the prerequisite introductory courses needed to successfully complete this course. Contact our team to know more.
- Big Data Analytics is an evolving science with new and unique applications cropping up every day. Check out our carefully planned and recommended sequence of training courses that will ensure you gain all the necessary skills needed in a systematic and meaningful way to succeed as a Data Scientist!
Our carefully planned and recommended learning structure for Data Science Series
|Level||Course Name||Know More|
|Basic I||Fundamentals of Statistics||Click here|
|Basic II||Data interpretation with BI Dashboards||Click here|
|Intermediate I||Learning R for Data Visualization and Analytics||Click here|
|Intermediate II||Big Data Analytics with R||Click here|
|Advanced I||Predictive Modeling & Machine Learning||Click here|
|Advanced II||Artificial Intelligence with Python||Click here|
|Advanced III||Using Big Data Analytics in Business||Click here|
Deep Learning Training Course Schedule
|Course||Location||Course Format||Start Date||End Date||Duration||Register|
- This course is offered as ,
- A Series of Three 5- Day Intensive Boot-Camp.
- Evening Classes. Two 2-hour sessions or One 4-Hour session per week
- As part of our Six Month Associate in Big Data Analytics.
- We currently offer this course Dubai and Abu Dhabi
- Checkout our course schedule more information.
- This course is offered as ,
- Participants must bring their own laptop. Preferably with more than 8GB ram.
- Our Artificial Intelligence training module will be mainly taught using Python as the programming language as well as some R-Programming.
- All softwares and Databases used for the course are open-source and participants will be taught how to install and set-up the environment.
- All training topics covered in the course will be taught using relevant industry specific case studies and examples.
- This training course is very hands-on/practical and is not a lecture or seminar. Participants will be expected to complete exercises and case studies on their own with necessary support and guidance from the instructor.