AI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware.
The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers — and the job salaries of Deep Learning practitioners — are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career.
Within this series of courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision.
Throughout this program you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers.
This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning.
ABOUT THIS COURSE
The course will teach you how to develop Deep Learning models using Pytorch while providing the necessary deep-learning background.
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.
In the final part of the course, we’ll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
WHAT YOU’LL LEARN
- 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.
DEEP LEARNING COURSE CONTENT
Module 1 – Introduction to Pytoch
- What’s Deep Learning and why Pytorch
- 1-D Tensors and useful Pytoch Functions
- 2-D Tensors and useful functions
- Derivatives and Graphs in Pytorch
- Data loader
Module 2 – Linear Regression
- Prediction 1D regression
- Training 1D regression
- Stochastic gradient descent, mini-batch gradient descent
- Train, test, split and early stopping
- Pytorch way
- Multiple Linear Regression
Module 3 – Classification
- Logistic Regression
- Training Logistic Regressions Part 1
- Training Logistic Regressions Part 2
- Softmax Regression
Module 4 – Neural Networks
- Introduction to Networks
- Network Shape Depth vs Width
- Back Propagation
- Activation functions
Module 5 – Deep Networks
- Batch normalization
- Other optimization methods
Module 6 – Computer Vision Networks
- Max Polling
- Convolutional Networks
- Pre trained Networks
Module 7 – Introduction to other Networks
- Introduction to Auto encoders
- Recurrent Neural Networks
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