Study and Job Info

4. ECLERX Off campus Drive | Data Analyst (AMP Data)

Placements:

1. Martdocks CSA 28.9.2023

2. Merkle Sokrati - Associate Business Analyst (2020 - 2024)

3. Walmat , Inc.

5. DP Worlds

6. L & T Software

Material

Deep Learning

1. Linear Algebra - Scalars, Vectors, Matrices and Tensors

Deep learning CNN

FIGMATIC |Software Engineer| Karnataka | BE/BTech |2023,2024 |

Qualifications

Specialized expertise in one or more areas, balanced with general knowledge. For example, proficiency in frontend technologies and a high-level understanding of how services handle HTTP requests.
Familiarity with code review practices and updating production systems safely.
Comfort navigating and managing work in substantial code bases.

Requirements

A Bachelor’s or Master’s degree in computer science or a related field, or equivalent practical experience.
Some experience with programming, gained from side projects, coursework, or internships. Fundamental knowledge and adaptability to new programming languages are key.
Experience from internships or collaborative multi-person coding projects (academic or professional setting).
Ability to learn unfamiliar systems independently, research, and work with mentors and experts.

Responsibilities

Collaborate on cross-functional projects with fellow engineers.
Provide valuable feedback on code reviews and technical designs.
Maintain and scale the systems your team operates to meet user demands.
Develop the skills to manage projects from inception to completion, acquiring project management and technical leadership skills.

Registration Link : Click Here

Benefits

Competitive salary ranging from 5-10 LPA.
Opportunities for professional growth and development.
Inclusive and motivating company culture.
Engaging work on cutting-edge projects within a collaborative environment.

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Home Previous

Internships:

4. ECLERX Off campus Drive | Data Analyst (AMP Data)

Placements:

1. Martdocks CSA 28.9.2023

2. Merkle Sokrati - Associate Business Analyst (2020 - 2024)

3. Walmat , Inc.

5. DP Worlds

6. L & T Software

Material

Deep Learning

1. Linear Algebra - Scalars, Vectors, Matrices and Tensors

Deep learning CNN

SMARTDOCS | SCT|MBA|BBA| 2022,2023,2024

Position: Service Consultant Trainee

Desirable Skills & Expertise:

We prefer candidates with 1-2 years of work experience but welcome fresh graduates as well.
Eligibility: MBA, BBA graduates, or fresh graduates without prior experience.
Enthusiastic about the opportunity to develop highly scalable and world-class products.
Demonstrates intellectual humility, combining intelligence, drive, creativity, and a willingness to learn from mistakes while lifting others up.
Possesses a keen business sense and can align it with organizational goals.
Demonstrates excellent communication and analytical skills, maintaining professionalism at all levels.
Strong leadership, organizational, planning, and project management skills are a must.
Ability to work independently and be self-directed.
Quick learner who can make significant individual contributions and manage multiple projects concurrently within a team environment.
Proactive, takes initiative, and drives tasks and issues to resolution.

Job Description:

Are you a dynamic and passionate individual ready to embark on an exciting career journey with the Global Innovator team? Do you possess the drive to excel and want to leverage your skills in the world of technology? At SmartDocs, we offer an unparalleled opportunity to launch your career in the tech industry. If you're looking for a company that values your ideas, appreciates your unique talents and contributions, and fosters a dynamic, flexible, and enjoyable work environment, then SmartDocs is the perfect place for you. We are deeply committed to our employees, clients, customers, our vibrant work culture, and, above all, our cutting-edge technology.

As a member of the Service Consultant team, you will play a pivotal role in empowering SmartDocs to deliver world-class technology solutions to our customers. We give preference to candidates with 1-2 years of work experience, but we also welcome fresh graduates who are enthusiastic about starting their careers in a fast-paced environment.

Registration Link : Click Here

Key Responsibilities:

Serve as a functional advisor to clients within the Solution Consultant team, translating business requirements into solution configurations and overseeing the overall solution delivery.
Create functional solution specifications and configure solutions based on business requirements using SmartDocs software products.
Ensure a smooth transition for clients from implementation to support phases.
Manage post-demo engagement until customers are satisfied with the product.
May be required to develop sales presentations, customer-facing collateral, cheat sheets, etc.
Proactively prepare and lead partners in solution adoption and innovation features.
Collaborate with internal teams, including product management, engineering, marketing, and delivery, to design solutions for customers.
Oversee multiple concurrent implementations, ensuring timelines are met and issues are promptly addressed while adhering to organizational standard operating procedures.
Highlight issues to designated team contacts and cross-functional teams to ensure timely resolution of client concerns.
Develop and maintain in-depth product knowledge.
Guide clients through various project stages and transition to the support organization.
Communicate project status to Services Managers and clients.
Review existing business processes and participate in the Process Improvement Program.
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Home Previous

Internships:

4. ECLERX Off campus Drive | Data Analyst (AMP Data)

Placements:

1. Martdocks CSA 28.9.2023

2. Merkle Sokrati - Associate Business Analyst (2020 - 2024)

3. Walmat , Inc.

5. DP Worlds

6. L & T Software

Material

Deep Learning

1. Linear Algebra - Scalars, Vectors, Matrices and Tensors

1. Introduction & Linear Algebra.

Deep learning CNN

Monday 25 September 2023

3. Deep Learning Basics Linear Algebra (Types of Matrix)

3. Types of Matrices:

1. Identity

2. Inverse Matrices

3. Diagonal

4. Symmetric

5. Orthagonal

Identity Matrix

An identity matrix is a square matrix of dimensions (n, n) having '1' across its main diagonal and '0' everywhere else. It is usually represented as 'I_n'

Example: The matrix shown below illustrates a (3, 3) Identity matrix

Python program

import numpy as np

#Creating an Identity matrix of size 2 using np.identity() function

identity_matrix_1 = np.identity(2)

print("Identity_matrix 1\n",identity_matrix_1)

Linear algebra offers a powerful tool called matrix inversion

To describe matrix inversion, we first need to define the concept of an identity matrix. An identity matrix is a matrix that does not change any vector when we multiply that vector by that matrix. We denote the identity matrix that preserves n-dimensional vectors as I_n.

The structure of the identity matrix is simple: all of the entries along the main diagonal are 1, while all of the other entries are zero.

The matrix inverse of A is denoted as A−1 , and it is defined as the matrix such that A⁻¹A = I_n.

Ax=b=A^-1x

Some special kinds of matrices and vectors are particularly useful.

Diagonal Matrix

A Square Matrix (D) is called a Diagonal Matrix, if D has zeros outside the main diagonal or principal diagonal. The Main diagonal or the principal diagonal are the elements on the diagonal that runs from the top left to bottom right.

Example: The matrix 'A' shown below illustrates a (3, 3) Diagonal matrix

We have already seen one example of a diagonal matrix: the identity matrix, where all of the diagonal entries are 1. We write diag(v) to denote a square diagonal matrix whose diagonal entries are given by the entries of the vector v. Diagonal matrices are of interest in part because multiplying by a diagonal matrix is very computationally efficient. To compute diag(v)x, we only need to scale each element xi by vi. In other words, diag(v)x = v . x. Inverting a square diagonal matrix is also efficient. The inverse exists only if every diagonal entry is nonzero, and in that case, diag(v)−1 = diag([1/v1, . . . , 1/vn ]^T)

Example Program in Python:

import numpy as np

# Creating a diagonal matrix with diagonal elements as (1,2,3)

diagonal_matrix = np.diag((1,2,3))

print(diagonal_matrix)

# Creating a diagonal matrix with a range of values

matrix_range= np.diag(np.arange(1,6,2))

print(matrix_range)

Symmetric

A symmetric matrix is any matrix that is equal to its own transpose: A = A^T

Python Program

import numpy as np

#Creating matrix A

A = np.array([[2,3,1], [3,4,-1], [1,-1,1]])

print("A:\t" , A)

# Finding the Transpose of the matrix

transposed_matrix = A.transpose()

print("Transpose of A:\n" , transposed_matrix)

comparison = (A == transposed_matrix)

#Checking if all the elements in the matrix comparision is true

equal_arrays = comparison.all()

print(equal_arrays)

Triangular Matrix

A triangular matrix can be either a lower triangular or an upper triangular matrix.

A lower triangular matrix is a square matrix in which all the elements above the main diagonal are zero.

Example: L is a lower triangular matrix of dimension (3, 3)

Orthogonal vectors

If the inner product of two non-zero vectors v₁ and v₂ is zero, that is

then, the vectors v₁ and v₂ are called orthogonal vectors.

Example: Let v₁ and v₂ be two vectors as follows:

import numpy as np

#Creating vectors

Vector_1 = np.array([[3],[-1],[2]])

Vector_2 = np.array([[2],[4],[-1]])

print("Vector 1\n",Vector_1)

print("Vector 2\n",Vector_2)

#Finding the transpose of Vector_1

trans = np.transpose(Vector_1)

#Finding the dot product

result = np.dot(trans,Vector_2)

print("Dot Product\n",result)

4. Norm

L^p

||x||_p = (∑|x_i|^p )^1/p

Norms, including the L^p norm, are functions mapping vectors to non-negative values. On an intuitive level, the norm of a vector x measures the distance from the origin to the point x.

More rigorously, a norm is any function f that satisfies the following properties:

• f (x) = 0 ⇒ x = 0

• f (x + y) ≤ f(x) + f (y) (the triangle inequality)

• ∀α ∈ R, f (αx) = |α|f(x)

The L² norm, with p = 2, is known as the Euclidean norm. It is simply the Euclidean distance from the origin to the point identified by x. The L 2 norm is used so frequently in machine learning that it is often denoted simply as ||x||, with the subscript 2 omitted. It is also common to measure the size of a vector using the squared L 2 norm, which can be calculated simply as x^Tx.

The L¹ norm may be simplified to ||x||₁ = (∑|x_i| )

________

PPTs

1. Introduction

2. Linear Algebra

Material in PDF

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Home Previous

Internships:

3. ECLERX Off campus Drive | Data Analyst (AMP Data)

Placements:

1. Merkle Sokrati - Associate Business Analyst (2020 - 2024)

2. Walmat , Inc.

4. DP Worlds

5. L & T Software

Material

Deep Learning

1. Linear Algebra - Scalars, Vectors, Matrices and Tensors