Deep Learning with Python and PyTorch

Deep learning with Python and PyTorch has emerged as a revolutionary force in the ever-changing face of technology, fueling discoveries in computer vision, natural language processing, and robotics. If you want to master the art of deep learning and realize the full potential of PyTorch, BITA Academy in Chennai provides the best training to provide you with the knowledge and abilities you need to succeed in this dynamic industry.

In this training, you will learn about artificial neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other cutting-edge deep learning approaches. Using PyTorch, a popular open-source deep learning framework, you’ll learn how to create and train neural networks for various applications, including image recognition, text analysis, and more.

What is PyTorch?

Facebook’s AI Research team developed PyTorch, an advanced open-source machine learning library. It is commonly regarded as a top choice among deep learning researchers and practitioners due to its versatility and ease of use. PyTorch enables developers to create and train neural networks for a wide range of applications, and it is particularly well-known for its support of dynamic computational graphs, which provides greater model flexibility. PyTorch allows you to unlock the full potential of deep learning, making it a must-have tool for anybody wishing to thrive in this discipline.

Roles and Responsibilities in PyTorch

PyTorch is important in many businesses and fields in deep learning and artificial intelligence. Understanding the tasks and responsibilities associated with PyTorch is essential for individuals interested in working in this industry. Here are some examples of crucial roles:

Deep Learning Engineer: Deep learning engineers utilize PyTorch to create and deploy neural network models for tasks such as image and speech recognition, natural language processing, and other applications.

Machine Learning Researcher: Researchers use PyTorch to experiment with and innovate on deep learning models, pushing the limits of what AI is capable of.

Data Scientist: Data scientists examine and model data using PyTorch, providing predictions and recommendations based on machine learning models.

AI Developer: AI developers work on projects that involve AI and machine learning components, building and training neural networks with PyTorch.

These roles may include data preprocessing, model construction, training, evaluation, and implementing machine learning models in production environments.

Syllabus of Deep Learning with Python and PyTorch

Part 1: Introduction to PyTorch

• PyTorch Fundamentals
• Why PyTorch
• Tensorflow vs Pytorch vs Keras
• Matrix Basics
• Reproducibility
• GPU and CPU Toggling

Part 2: PyTorch Fundamentals

• Basic Mathematical Tensor Operations
• PyTorch Tensors
• Two-Dimensional Tensors
• Variables and Gradients
• Practical Applications Using PyTorch

Part 3: Linear Regression PyTorch Way

• What is Linear regression?
• Linear Regression in PyTorch
• Linear Regression From CPU to GPU in PyTorch
• Multiple Input Output Linear Regression

Part 4: Logistic Regression with PyTorch

• Logistic Regression for classification
• Going deeper into Logistic Regression
• Logistic Regression with PyTorch
• From CPU to GPU in PyTorch

Part 5: Basic Neural Network with PyTorch

• Hidden Layers
• Backpropagation
• Activation Functions
• Non-linearity
• Feedforward Neural Network in PyTorch
• More Feedforward Neural Network Models in PyTorch
• Feedforward Neural Network From CPU to GPU in PyTorch

Part 6: Convolutional Neural Network (CNN) with PyTorch

• Feedforward Neural Network Transition to CNN
• One Convolutional Layer
• Multiple Convolutional Layers Overview
• Pooling Layers
• Padding for Convolutional Layers
• Output Size Calculation
• CNN in PyTorch
• More CNN Models in PyTorch
• Expanding CNN Model’s Capacity
• From CPU to GPU in PyTorch
• Pre-trained CNNs

Part 7: Recurrent Neural Networks (RNN) with PyTorch

• Introduction to RNN
• RNN in PyTorch
• More RNN Models in PyTorch
• RNN From CPU to GPU in PyTorch

Part 8: Long Short-Term Memory Networks (LSTM) with PyTorch

• Introduction to LSTMs
• LSTM Equations
• LSTM in PyTorch
• More LSTM Models in PyTorch

PyTorch Certification Training 

PyTorch certification training makes it easier to get experience in PyTorch. This specialized training program will provide the information and practical skills you need to succeed in deep learning with Python and PyTorch. 

A PyTorch certification confers numerous advantages that can profoundly influence one’s professional trajectory within deep learning and machine learning. The PyTorch certification provides concrete evidence of an individual’s competence and proficiency in utilizing PyTorch. The successful demonstration of effectively using this deep learning framework validates one’s ability, bolstering one’s reputation among prospective employers and colleagues. The acquisition of certification can play a pivotal role in facilitating job advancement. Obtaining Deep Learning with Python and PyTorch Certification enhances the likelihood of eligibility for career advancements, income increments, and higher-level employment responsibilities. This displays an individual’s dedication to ongoing education and improvement in their professional development.

• Deep Learning Specialization by deeplearning.ai
• Udacity’s Deep Learning Nanodegree
• PyTorch Scholarship Challenge by Facebook
• edX’s Deep Learning with PyTorch

Job Opportunities in PyTorch 

The demand for professionals with PyTorch experience is increasing. Companies from various industries seek someone who can use PyTorch to create creative solutions and drive AI efforts. Deep learning with Python and PyTorch job opportunities are diverse and include positions such as:

• Deep Learning Engineer
• Machine Learning Researcher
• Data Scientist
• AI Developer

Suppose you have a PyTorch certification and the necessary skills. In that case, you’ll be well-prepared to acquire fascinating positions in these sectors and contribute to the ever-evolving world of artificial intelligence and deep learning. Your PyTorch experience will open doors to various professional prospects, from research to development.

Why should you select us?

• After completing Deep Learning with Python and PyTorch, you will learn how to create and train neural networks for various applications, including image recognition, text analysis, and more.
• We offer deep learning with Python and PyTorch for professionals and students who want to start their careers in web scraping.
• Our trainer’s teaching skill is excellent, and they are very polite when clearing doubts.
• We conduct mock tests that will be useful for your PyTorch interview preparation.
• Even after completing your deep learning with Python and PyTorch, you will get lifetime support from us.
• We know the IT market, and our PyTorch content aligns with the latest trends.
• We provide classroom training with all essential preventative precautions.
• We provide PyTorch online training in live meetings with recordings.