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Deep Learning Course

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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

|| Choose Deep Learning Course From BIT

Features of BIT Coaching Classes ,Comprehensive Curriculum in BIT ,Industry Collaboration at BIT ,State of the art facility at bit ,Expert Faculty Of BIt ,Project Based Approach,Advantages of taking Admission at 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

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|| 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

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|| 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

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|| 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

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|| 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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Shubham Ambike
4

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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Kunj Patel
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Kunj Patel, a proud BIT graduate, is excelling as a Monitoring and Evaluation Coordinator at Deepak Foundation. BIT continues to prove itself as the best institute for Mechanical Engineering by not only offering great placement opportunities but also by building strong technical and software skills that prepare students for a successful future.

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Rutvik Panchal
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Placed as a Data Associate at Numerator, Rutvik Panchal, a proud BIT graduate, stands out with his strong technical and analytical skills. Best institute for Data Science ! BIT doesn’t just give placements—they make you technically strong and skilled in software. Thank you, BIT!

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placement report

|| 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.

Our student projects

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Vidit Modi
Project Title: Pockemon Machine Learning Project
Data Science
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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

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

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Rutvik Panchal
Project Title: Air Quality Project

Data Science
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Tirth Modi
Project Title: Abalone Age Prediction

Data Science

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