Deep Learning Career Options for Students in Faridabad

Recommended learning order:

Weeks 1-2 — Python + maths refresher
- functions, classes, exceptions
- NumPy arrays and vectorised operations
- linear algebra: vectors, matrices, dot products
- probability: distributions, expected value

Weeks 3-4 — ML fundamentals
- train/test split, cross-validation
- classification and regression metrics
- overfitting and regularisation
- baseline models with scikit-learn

Weeks 5-6 — Deep learning basics
- neural networks and backpropagation
- activation functions and loss functions
- optimisers (SGD, Adam)
- batch size, learning rate, epochs

Weeks 7-9 — Framework practice
- PyTorch or TensorFlow (pick one)
- datasets, dataloaders, model classes
- training loop, validation loop
- saving and loading checkpoints

Weeks 10-11 — Specialise
- CNN for image classification, or
- Transformer for text classification

Weeks 12-13 — MLOps basics
- wrap the model in an API
- containerise with Docker
- deploy to a cloud endpoint
- monitor basic metrics

First model to build — simple version:

Goal: classify images or text in a
public dataset.

Steps:
1. Pick a public dataset (CIFAR-10,
   IMDB reviews, or a Kaggle set).
2. Split train / validation / test.
3. Start with a small baseline model.
4. Train for a few epochs and record
   accuracy and loss.
5. Plot training and validation curves.
6. Save the model and reload it.
7. Serve predictions from a script.

Then improve:
- add data augmentation
- try a pretrained backbone
- tune learning rate
- add early stopping

Report honestly:
- accuracy on the test set
- what you did NOT try
- where the model fails (misclassified
  examples)

Where to look for deep learning roles:

1. LinkedIn
   - filter by "Delhi NCR" and "Remote India"
   - search "ML engineer", "AI engineer",
     "computer vision", "NLP"

2. Company career pages
   - Delhi NCR product companies
   - Faridabad manufacturing groups
     building in-house AI teams

3. Job boards
   - Naukri (Delhi NCR filter)
   - Instahyre, Cutshort
   - Remote-specific boards for remote roles

4. Kaggle and GitHub communities
   - hiring managers post roles in communities
   - people who recognise your project may
     share openings

5. Referrals
   - NCR alumni from your college
   - people you meet in meetups
   - people whose papers or projects you
     have followed online

How to frame your location:

If the role is in Delhi NCR:
- say you are in Faridabad
- state commute time honestly
- mention hybrid or relocation willingness
  if relevant

If the role is remote:
- say "based in Faridabad, available
  for remote work across India"
- mention timezone alignment
- avoid making location sound like a
  limitation

If the role is in Faridabad:
- mention local factories, logistics
  companies or offices you have visited
- show that you understand the local
  industry context

Never lie about being in Delhi
when you are in Faridabad.
It will be discovered and it will
damage trust.

Project structure for a deep learning repo:

1. Problem statement
   - What are you predicting and why?
   - What does success look like?

2. Data
   - source, size, classes
   - train / validation / test split
   - cleaning decisions

3. Model
   - baseline first, then improvement
   - architecture, loss, optimiser
   - hyperparameters and stopping rule

4. Training
   - training curves
   - validation accuracy per epoch
   - final test metrics

5. Failure analysis
   - 5 misclassified examples
   - what the model confuses
   - what you would try next

6. Deployment (if applicable)
   - inference script or API
   - input format and example
   - Docker instructions

7. Reproducibility
   - requirements.txt
   - random seeds
   - training command

README checklist for a deep learning project:

□ Problem stated in one sentence
□ Data source and size listed
□ Train / test split described
□ Baseline model shown
□ Final model and metrics shown
□ Training curves included as images
□ Failure examples included
□ Requirements and setup steps
□ Training command documented
□ Limitations stated honestly

Nice extras:
- evaluation on a second dataset
- inference latency noted
- a small demo (Gradio or Streamlit)
- a short video walkthrough

Avoid:
- only screenshots of code
- no metrics at all
- "99% accuracy" without context
- no reproducibility instructions

90-Day — Daily Schedule:

7:00 AM — Wake up, quick review of yesterday
8:00 AM — One clear outcome for today
9:00 AM — Deep work block 1 (90 min)
   - Week 1-2: Python and ML fundamentals
   - Week 3-4: neural networks and training loops
   - Week 5-6: Project 1 (vision)
   - Week 7-8: Project 2 (NLP)
   - Week 9: Project 3 (deployment)
   - Week 10-12: portfolio, applications, interviews
12:00 PM — Break
1:00 PM — Practice block (60 min)
   - reproduce an example
   - tune a hyperparameter
   - write a short README section
5:00 PM — Review notes, update tracker
6:00 PM — Exercise or rest
8:00 PM — 20 min reading (papers or blogs)
10:00 PM — Wind down

Weekly review questions:

□ Did I finish the week's one outcome?
□ Can I explain what I learned without notes?
□ Did I commit something to GitHub?
□ Did I train a model on real data this week?
□ What blocked me, and what will I change next week?

End-of-month checkpoints:

Day 30:
□ Python and ML fundamentals solid
□ MNIST trained end to end
□ Training loop understood

Day 60:
□ Two projects published
□ One vision project, one NLP project
□ READMEs written

Day 90:
□ Three projects published
□ One deployed behind an API
□ Resume and portfolio ready
□ Applications sent