...

Full Bio

Data Scientist-Multitasking Job

today

Data Scientist-A right path

today

Machine Learning can be the future now

today

Is it beneficial! Implementing AI at the workplace

today

A way for a Linux user

3 days ago

Every Programmer should strive for reading these 5 books

557796 views

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

207189 views

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

146787 views

Highest Paid Programming Languages With Highest Market Demand

129546 views

Python Programming Language can easily be acquired easily. How?

118287 views

### Some Deep Learning courses to keep in mind

- We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
- We'll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.

- 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.

- Explain foundational TensorFlow concepts such as the main functions, operations, and 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.

- 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.