...

Full Bio

Some major features of Java

today

Deep Learning-Impact on Image Recognition and Speech Patterns

today

The easiest way to backup your Data

today

Data Scientist-Multitasking Job

yesterday

Data Scientist-A right path

yesterday

Every Programmer should strive for reading these 5 books

557856 views

Why you should not become a Programmer or not learn Programming Language?

207255 views

See the Salaries if you are willing to get a Job in Programming Languages without a degree?

146799 views

Highest Paid Programming Languages With Highest Market Demand

129561 views

Python Programming Language can easily be acquired easily. How?

118308 views

### Learn Top Online Deep Learning Courses to have good hands-on Algorithms

- Explain foundational TensorFlow concepts such as the main functions, operations, and the execution pipelines.
- Describe how TensorFlow can be used in curve fitting, regression, classification, and minimization of error functions.
- Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
- Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

- The components of a deep neural network and how they work together
- The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
- A working knowledge of vocabulary, concepts, and algorithms used in deep learning

- You will learn about exciting applications of deep learning and why it is really rewarding to learn how to leverage deep learning skills.
- You will learn about neural networks and how they learn and update their weights and biases.
- You will learn about the vanishing gradient problem.
- You will learn about building a regression model using the Keras library.
- You will learn about building a classification model using the Keras library.
- You will learn about supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library.
- You will learn about unsupervised learning models such as autoencoders.

- Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
- Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
- Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
- Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
- Master Deep Learning at scale with accelerated hardware and GPUs.

- Explain and apply knowledge of Deep Neural Networks and related machine learning methods;
- Know how to use Python, and Python libraries such as Numpy and Pandas along with the PyTorch library for Deep Learning applications;
- Build Deep Neural Networks using PyTorch.