Deep Learning Course in Dahod

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Deep Learning courses provide a comprehensive introduction to neural networks and their AI applications. Covering fundamental concepts such as neural network architecture, backpropagation, and activation functions, students learn to implement and train models using frameworks like TensorFlow and PyTorch. Key topics include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced techniques like transfer learning and generative adversarial networks (GANs). Hands-on projects help students apply deep learning to real-world problems in computer vision, natural language processing, and more. By course end, learners gain practical skills and a solid understanding of deep learning, ready to tackle complex AI challenges.
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Deep Learning Course

|| Deep Learning Course in Dahod


The Deep Learning Course at BIT Dahod is a transformative learning experience aimed at unraveling the complexities of artificial intelligence by focusing on neural networks and their groundbreaking capabilities. This course is structured to help students move beyond traditional machine learning and dive deep into the layered structures that power technologies like self-driving cars, voice assistants, and advanced recommendation systems. It starts with the fundamentals of neural networks and gradually explores deeper architectures such as Convolutional Neural Networks (CNNs) for image processing, Recurrent Neural Networks (RNNs) for sequence data, and Long Short-Term Memory (LSTM) units for time-series analysis. Learners are introduced to industry-standard tools like TensorFlow and Keras, using them to build, train, and optimize deep learning models through hands-on labs and real-world projects. The course also demystifies concepts like overfitting, dropout, backpropagation, and gradient descent, ensuring students understand both the intuition and math behind each model. What sets this course apart is its emphasis on real-world problem-solving—students tackle challenges like medical image classification, language translation, and even creating generative models using GANs. As the course progresses, learners also explore advanced topics such as transfer learning and model tuning, preparing them to create robust and scalable AI solutions. With an approach that blends theory, coding, and creativity, BIT Dahod’s Deep Learning course is perfect for those looking to specialize in AI and lead innovation in industries such as healthcare, automation, cybersecurity, and beyond.

|| Choose Deep Learning Course From BIT

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|| What will I learn?

  • Understand the fundamental principles of machine learning and its applications.
  • Implement machine learning algorithms using programming languages such as Python.
  • Utilize advanced machine learning methods including ensemble techniques and neural networks.
  • Perform data preprocessing and feature engineering.
  • Evaluate and validate machine learning models using appropriate metrics.
  • Apply supervised and unsupervised learning techniques to solve various problems.

|| What will I learn?

  • Understand the fundamental principles of machine learning and its applications.
  • Implement machine learning algorithms using programming languages such as Python.
  • Utilize advanced machine learning methods including ensemble techniques and neural networks.
  • Perform data preprocessing and feature engineering.
  • Evaluate and validate machine learning models using appropriate metrics.
  • Apply supervised and unsupervised learning techniques to solve various problems.

|| Requirements

  • Basic programming knowledge (preferably in Python)
  • Understanding of fundamental statistical concepts
  • Basic knowledge of linear algebra and calculus

|| Requirements

  • Basic programming knowledge (preferably in Python)
  • Understanding of fundamental statistical concepts
  • Basic knowledge of linear algebra and calculus



    A deep learning course typically delves into a comprehensive array of topics essential for understanding and mastering neural networks. It typically begins with an exploration of the historical context and biological foundations of neural networks, transitioning into the basics of artificial neurons, activation functions, and feedforward neural networks. As students progress, they encounter convolutional neural networks (CNNs) tailored for image processing tasks, recurrent neural networks (RNNs) designed for sequential data analysis, and advanced architectures such as attention mechanisms and transformers. The curriculum often includes in-depth discussions on optimization techniques like gradient descent and regularization methods like dropout to enhance model performance and prevent overfitting. Practical applications across various domains, from computer vision to natural language processing and reinforcement learning, are thoroughly explored. Additionally, ethical considerations surrounding bias, privacy, and responsible AI development are woven into the fabric of the course. Hands-on projects and labs provide students with the opportunity to implement theoretical concepts into practice, utilizing popular deep learning libraries and tools like TensorFlow, PyTorch, and Keras. Through this multifaceted approach, students gain both theoretical knowledge and practical skills necessary to tackle real-world challenges in the field of deep learning.

    Deep Learning Learning Pathways ,Deep Learning Roadmap ,ANN ,Tensorflow,CNN,RNN,KERAS ,



    Artificial Neural Network (ANN)

    • Biological and Artificial Neurons
    • Activation Functions
    • Perceptron
    • Feed Forward Network
    • Multilayer Perceptron (MLP)
    • Back Propagation, Deep ANN
    • Optimisation Algorithms
    • Gradient Descent
    • Stochastic Gradient Descent (SGD)
    • MiniBatch Stochastic Gradient Descent
    • Stochastic Gradient Descent with Momentum
    • AdaGrad, RMSProp , Adam
    • Batch Normalisation

    • KERAS
    • What is Keras?
    • How to Install Keras?
    • Why to Use Keras?
    • Different Models of Keras
    • Preprocessing Methods
    • What are the Layers in Keras?

    • Tensorflow 2.0
    • TensorFlow in Realtime Applications
    • Advantages of TensorFlow
    • How to Install TensorFlow
    • TensorFlow 1x vs TensorFlow 2.0
    • Eager Execution in TensorFlow 2.0

    • Convolutional Neural Network(CNN)
    • Introduction to Computer Vision
    • Convolutional Neural Network
    • Architecture of Convolutional network
    • Image as a Matrix, Convolutional Layer
    • Feature Detector & Feature Maps
    • Pooling Layer, Max Pooling
    • Min Pooling, 
    • Avg Pooling
    • Flattening Layer, Padding, Striding
    • Image Augmentation
    • Basics of Digital Images

    • Recurrent Neural Network (RNN)
    • RNN Network Structure
    • Different Types of RNNs
    • Bidirectional RNN
    • Limitations of RNN

    In a Deep Learning course, project training features typically include:


    • Understanding Neural Networks: Students start by understanding the fundamentals of neural networks, including perceptrons, activation functions, and feedforward propagation. They also learn about different types of neural networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
    • Model Architecture Design: Students learn how to design deep learning architectures tailored to specific tasks and datasets. They explore techniques for building deep neural networks with multiple layers, including convolutional layers, pooling layers, recurrent layers, and dense layers.
    • Training Techniques: Training deep learning models requires specialized techniques to optimize performance and prevent issues like overfitting. Students learn about techniques such as gradient descent optimization algorithms (e.g., SGD, Adam), regularization methods (e.g., dropout, L2 regularization), and learning rate scheduling.
    • Hyperparameter Tuning: Deep learning models often have several hyperparameters that need to be tuned to achieve optimal performance. Students learn how to tune hyperparameters systematically using techniques like grid search, random search, and Bayesian optimization.
    • Transfer Learning and Pretrained Models: Transfer learning is a powerful technique in deep learning where pre-trained models are fine-tuned for new tasks. Students learn how to leverage pre-trained models from libraries like TensorFlow and PyTorch and adapt them to their own projects.
    • Handling Large Datasets: Deep learning projects often involve working with large datasets that may not fit into memory. Students learn strategies for handling large datasets efficiently, including data augmentation, batch loading, and distributed training.
    • Evaluation and Validation: Students learn how to evaluate the performance of deep learning models using appropriate metrics such as accuracy, precision, recall, F1-score, and ROC curves. They also learn about techniques for cross-validation and assessing model uncertainty.
    • Deep Learning Libraries and Frameworks: Students gain practical experience with popular deep learning libraries and frameworks such as TensorFlow, Keras, PyTorch, and MXNet. They learn how to use these libraries to implement deep learning models, train them on real-world datasets, and evaluate their performance.
    • Project-Based Learning: Hands-on projects are integral to the course, allowing students to apply their knowledge and skills to real-world problems. They work on end-to-end deep learning projects, from data preprocessing and model training to evaluation and deployment.
    • Ethical Considerations: The course covers ethical considerations in deep learning, including fairness, bias, privacy, and transparency. Students learn about the ethical implications of their models and how to mitigate potential biases and risks.


    Overall, the project training features in a Deep Learning course provide students with a comprehensive understanding of deep learning concepts and practical skills to tackle real-world problems effectively.

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|| Skills to Master

Skills to Master:
Python:
Linear Algebra:
Calculus:
Convolutional Neural Networks (CNNs):
Recurrent Neural Networks (RNNs):
Generative Models:
Sequence-to-Sequence Models:
Reinforcement Learning:
Data Preprocessing:
Computer Vision:
Natural Language Processing (NLP):

|| Tools to Master

PYTHON PYTHON
Pandas Pandas
GitHub GitHub
Keras Keras
NumPy NumPy
PyTorch PyTorch
Seaborn Seaborn
Tensor Flow Tensor Flow
CNN CNN
Kaggle Kernels Kaggle Kernels
Matplotlib Matplotlib

|| Deep Learning Career Opportunities in India

India offers a wealth of opportunities in the field of deep learning, driven by its rapidly growing tech industry, burgeoning startup ecosystem, and increasing adoption of artificial intelligence across various sectors. Here are some of the key opportunities in India for deep learning:


  • Research and Development: Indian academic institutions and research organizations are actively involved in deep learning research. Opportunities exist for researchers to contribute to cutting-edge projects in areas like computer vision, natural language processing, and healthcare analytics.
  • Tech Industry: India's technology sector, including giants like Tata Consultancy Services (TCS), Infosys, and Wipro, as well as multinational companies with research centers in India like Google, Microsoft, and Amazon, are investing heavily in AI and deep learning. There are abundant opportunities for deep learning engineers and researchers in these companies, both in product development and research roles.
  • Startups: India has seen a surge in AI startups in recent years, with many focusing on deep learning applications. These startups span various sectors including healthcare, e-commerce, finance, agriculture, and education. Joining a deep tech startup offers opportunities to work on cutting-edge projects, collaborate with talented teams, and make a significant impact.
  • Government Initiatives: The Indian government has launched initiatives to promote AI research and adoption, such as the National AI Portal and the National Programme on AI. These initiatives create opportunities for professionals to contribute to large-scale AI projects and policy development.
  • Education and Training: With the increasing demand for skilled AI professionals, there is a growing need for quality education and training programs in deep learning. Institutes, both public and private, offer courses and workshops on deep learning, providing opportunities for professionals to upskill and stay abreast of the latest developments in the field.
  • Freelancing and Consulting: Deep learning professionals in India can leverage platforms like Upwork, Freelancer, and Toptal to offer their expertise as freelancers or consultants to clients worldwide. This provides flexibility in work arrangements and the opportunity to work on diverse projects across different industries.
  • Entrepreneurship: India's vibrant startup ecosystem presents opportunities for entrepreneurs to build AI-powered products and services. Deep learning expertise is crucial for developing innovative solutions that address market needs and challenges.


Overall, India offers a fertile ground for deep learning professionals to thrive, innovate, and contribute to the advancement of AI technology both domestically and globally.

|| Scope of Deep Learning Course

scope of deep learning ,Scope of Deep learning in india ,information technology and software development ,Healthcare and medical diagnostics,E commerce and Retail ,finance and banking

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|| Deep Learning Career Option

Completing a deep learning course can open up a variety of job roles in the technology and data science sectors. Here are some of the key positions you might pursue:



  • Deep Learning Engineer: Develop and implement deep learning models for various applications such as image recognition, natural language processing, and autonomous systems. Expertise in neural networks, experience with frameworks like TensorFlow or PyTorch, and strong programming skills in Python.
  • Machine Learning Engineer: Design and develop machine learning models, including deep learning algorithms, to solve complex problems. Proficiency in machine learning algorithms, experience with ML frameworks, and strong analytical skills.
  • Data Scientist: Analyze large datasets to extract insights, develop predictive models, and communicate findings to stakeholders.: Statistical analysis, machine learning, data visualization, and proficiency in tools like R, Python, and SQL.
  • AI Research Scientist: Conduct cutting-edge research in AI and machine learning, publish findings, and develop new algorithms and models. Advanced knowledge of AI and machine learning theories, strong mathematical background, and experience in research methodologies.
  • Computer Vision Engineer: Develop and implement computer vision algorithms for applications such as object detection, facial recognition, and augmented reality. Experience with computer vision techniques, deep learning frameworks, and image processing.
  • NLP Engineer (Natural Language Processing): Develop models for understanding and generating human language, such as chatbots, translation systems, and sentiment analysis tools. Expertise in NLP techniques, deep learning, and text processing libraries.
  • Data Engineer: Design, construct, and maintain large-scale data processing systems and ensure efficient data flow for analytics. Knowledge of data warehousing, ETL processes, and big data technologies like Hadoop and Spark.
  • AI Product Manager: Oversee the development and implementation of AI-powered products, from conception to launch, and ensure alignment with business goals. Understanding of AI technologies, project management, and strong communication skills.
  • Robotics Engineer: Design and develop robotic systems that can perform tasks autonomously or semi-autonomously using AI and machine learning. Robotics, deep learning, control systems, and experience with hardware integration.
  • AI Consultant: Advise organizations on how to leverage AI and deep learning technologies to solve business problems and improve processes. Deep understanding of AI applications, strong problem-solving skills, and the ability to communicate technical concepts to non-technical stakeholders.

|| Job Roles and Salary

job roles deep learning ,deep learning engineer ,machine learning engineer ,data scientist,computer vision engineer ,data engineer

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|| Average Salary for Deep Learning Professionals in India

The average salary for professionals working in the field of deep learning in India can vary widely based on factors such as location, experience, level of expertise, education, industry, and the specific role within deep learning. However, to provide a general idea:


  • Entry-level Positions: Entry-level professionals or fresh graduates with a basic understanding of deep learning concepts can expect to earn an average salary ranging from ₹3,00,000 to ₹6,00,000 per annum.
  • Mid-level Positions: Mid-level professionals with a few years of experience in deep learning, proficient in implementing neural networks, and capable of working on real-world projects can earn an average salary ranging from ₹6,00,000 to ₹15,00,000 per annum.
  • Senior-level Positions: Senior-level professionals, such as deep learning engineers, researchers, or project leads, with significant expertise in advanced deep learning techniques, extensive experience in developing complex models, and a track record of successful projects, can earn an average salary ranging from ₹15,00,000 to ₹40,00,000 per annum or more, depending on their level of responsibility and the organization's size and reputation.


It's important to note that salaries in the field of deep learning can vary significantly based on factors such as the company's location (e.g., metropolitan cities tend to offer higher salaries), the industry sector (e.g., technology, finance, healthcare), the candidate's academic qualifications (e.g., master's or doctoral degree in computer science, machine learning, or related fields), certifications, publications, and additional skills (e.g., proficiency in programming languages like Python, experience with deep learning frameworks like TensorFlow or PyTorch).

Additionally, as deep learning continues to evolve and gain prominence in various industries, the demand for skilled professionals in this field is expected to increase, potentially leading to higher salaries and greater career opportunities for individuals with expertise in deep learning.

 

|| Top Hiring Companies

Hiring Companies ,Top Companies ,Job Placement ,TCS,EY ,HCL ,NUMERATOR ,INFOSYS ,L&T ,MICROSOFT ,Top Hiring Companies at BIT , Top Placement Companies at BIT

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|| Some Prominent Companies in India that use Deep Learning Technology

Several companies in India actively utilize deep learning technologies and frequently seek professionals with deep learning skills. Here are some prominent ones across various sectors:

Information Technology and Software Development


Tata Consultancy Services (TCS)

  • Infosys
  • Wipro
  • HCL Technologies
  • Tech Mahindra


E-commerce and Retail

  • Flipkart
  • Amazon India
  • Myntra
  • Snapdeal


Finance and Banking

  • HDFC Bank
  • ICICI Bank
  • Axis Bank
  • State Bank of India (SBI)
  • Paytm


Healthcare and Medical Diagnostics

  • Niramai
  • Qure.ai
  • SigTuple
  • Innovaccer


Telecommunications

  • Reliance Jio
  • Bharti Airtel
  • Vodafone Idea


Automotive and Transportation

  • Tata Motors
  • Mahindra & Mahindra
  • Ola (ANI Technologies)
  • Maruti Suzuki
  • Agriculture


CropIn Technology

  • SatSure
  • AgNext
  • Intello Labs
  • EdTech
  • BYJU'S
  • Unacademy
  • Vedantu
  • UpGrad


Startups and Innovation

  • Zoho Corporation
  • Freshworks
  • Fractal Analytics
  • Haptik
  • Manthan Systems


Government and Public Sector

  • National Informatics Centre (NIC)
  • Centre for Development of Advanced Computing (C-DAC)
  • Indian Space Research Organisation (ISRO)

|| Deep Learning holds a prominent position in the Indian Job Market

In India, the field of deep learning is experiencing rapid growth and offers promising placement opportunities for individuals with expertise in this domain. With the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries, there is a high demand for professionals skilled in deep learning algorithms and techniques. Major technology companies, research institutions, and startups in cities like Bangalore, Hyderabad, Mumbai, and Delhi are actively seeking talent proficient in deep learning to drive innovation in areas such as computer vision, natural language processing, and autonomous systems.


India's robust educational infrastructure, including prestigious institutes like the Indian Institutes of Technology (IITs) and Indian Institutes of Science (IISc), offer specialized programs and research opportunities in deep learning and related fields. Furthermore, online platforms and MOOCs (Massive Open Online Courses) provide accessible avenues for individuals to upskill and stay updated with the latest advancements in deep learning.


Additionally, India's vibrant startup ecosystem fosters innovation and entrepreneurship in AI and deep learning, creating opportunities for professionals to work on cutting-edge projects and contribute to groundbreaking developments. Moreover, multinational corporations with research and development centers in India often recruit top talent in deep learning to drive their AI initiatives globally.


As India continues to position itself as a hub for technology and innovation, the demand for deep learning expertise is expected to grow further, offering exciting career prospects and placement opportunities for aspiring professionals in the field.


|| Empowering Your Career Transition From Learning To Leading

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Megha Bhatt

Megha Bhatt, an ML Engineer at Cognizant, demonstrates prowess by leveraging unique tools such as Alteryx for advanced data blending and Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

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Darshna Dave

Darshna Dave, excelling as a Data Analyst at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

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Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-Business Analytics course, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

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Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post learning Data Analytics course, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

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Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ after studying a Full-Stack Data science course from BIT. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, which showcases his expertise in essential data processing tools.

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|| Get  Deep Learning Certification

Three easy steps will unlock your Deep Learning Certification:

 

  • Finish the online / offline course of Deep Learning   Course and the Assignment
  • Take on and successfully complete a number of industry-based Projects
  • Pass the Deep Learning   certification exam

 

The certificate for this Deep Learning   course will be sent to you through our learning management system, where you can also download it. Add  a link to your certificate to your CV or LinkedIn profile.

 

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|| Frequently asked question

A Deep Learning Course focuses on teaching participants the theory, algorithms, and applications of deep learning, a subset of machine learning that deals with neural networks comprising multiple layers. It covers topics such as artificial neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their applications in various domains.

This course is suitable for individuals interested in pursuing a career in deep learning, artificial intelligence, or related fields. It caters to beginners with some background in machine learning as well as professionals looking to specialize in deep learning techniques and applications.

Prerequisites may include a solid understanding of machine learning concepts and algorithms, proficiency in a programming language such as Python, and familiarity with linear algebra, calculus, and probability theory. Some courses may also require prior experience with neural networks or deep learning frameworks.

Most reputable Deep Learning courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

Some courses may offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities. However, this varies depending on the course provider.

After completing the course, you may continue to have access to resources such as course materials, coding exercises, alumni networks, coding communities, and additional learning resources. Some providers offer lifetime access to course materials or alumni benefits to support your continued growth and success.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

Typically, you should have a solid understanding of programming (especially Python), linear algebra, calculus, and probability theory. Familiarity with basic machine learning concepts and algorithms is also beneficial.

Prerequisites often include a strong foundation in mathematics (linear algebra, calculus, probability), proficiency in programming (especially Python), and familiarity with basic machine learning concepts.

Courses typically include hands-on projects where you implement deep learning algorithms and architectures on real-world datasets. These projects help reinforce learning and build a portfolio of practical experience.

Graduates of Deep Learning courses can pursue roles such as deep learning engineer, machine learning researcher, AI scientist, computer vision engineer, or data scientist specializing in neural networks and AI.

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Our student projects

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Kunj Patel
Project Title: Online Payment Fraud Detection

Data Science
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Vidit Modi
Project Title: Brain Tumor Classification

Data Science
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Project Title: Air Quality Project

Data Science
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Tirth Modi
Project Title: Abalone Age Prediction

Data Science
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Vidit Modi
Project Title: Pockemon Machine Learning Project

Data Science

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