Day 3 of 60 Days of Deep Learning with Projects Series
With Projects and Examples…
Welcome back peeps. Hope all’s well. This week I’m targeting to finish at-least day 7 of Deep learning in my free time post work and over the weekend.
System Design Case Studies — In Depth
Design Instagram
Design Messenger App
Design Twitter
Design URL Shortener
Design Dropbox
Mega Compilation : Solved System Design Case studies
In the last post Day 1 and Day 2 of Deep Learning with Project Series we covered the deep learning basics .
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
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Day 1 of 60 Days of Deep Learning with Projects Series
Day 2 of 60 Days of Deep Learning with Projects Series
In this post, we will discuss what do you need to get started in deep learning once you have completed day 1 and day 2.
Lets dive in Day 3 of Deep Learning Series !
1. Python
Find the basic python post : Link
In this post we have covered —
1. Data types, strings, operators, and Chaining Comparison Operators with Logical Operators
2. Python Lists and Dictionaries, Sets, Tuples
3. Loops, Break and Continue Statements
4. Object-Oriented Programming — Class and attributes
5. Python strings in detail
6. Python F-String
7. Map, Classes, Functions and Arguments
8. First Class functions, Private Variables, Global and Non Local Variables, __import__ function
9. Magic Functions, Tuple Unpacking
10. Static Variables and Methods in Python
11. Lambda Functions, Magic methods
12. Inheritance and Polymorphism, Errors and Exception Handling
13. User-defined functions, Python garbage collection, debugger in Python
14. Iterators, Generators, and Decorators, Memoization using Decorators
15. Ordered and Defaultdict, Coroutine
16. Regular expression, Magic methods, Closures
17. ChainMap
18. Python Itertools
19. Advanced python constructs
20. Comprehensions, Named Tuple, Type hinting in Python
2. Advanced Python
Find the advanced python post : Link
In this post, we have covered —
Static Variables and Methods in Python
Lambda Functions, Magic methods
Inheritance and Polymorphism, Errors and Exception Handling
User-defined functions, Python garbage collection, debugger in Python
Iterators, Generators, and Decorators, Memoization using Decorators
Ordered and Defaultdict, Coroutine
Regular expression, Magic methods, Closures
ChainMap
Python Itertools
Advanced python constructs
3. Techniques to write efficient and optimized code
Find the techniques to write efficient code post : Link
4. Pandas
Pandas is a a fast, powerful, flexible and easy to use open source data analysis and manipulation tool. It’s a newer package built on top of NumPy, and provides an efficient implementation of a DataFrame.
Pandas is an open source Python package written for the Python programming language for data manipulation, analysis and ML tasks
It is built on top of another package named Numpy, which provides support for mathematical computations and multi-dimensional arrays.
Find the Pandas and advanced Pandas post : Link
5. Numpy
Numpy is a python library for scientific computing — to work with multidimensional array objects and used to handle large amount of data. An array which is a grid of values and is indexed by a tuple of nonnegative integers is main data structure of the Numpy library. ndarray is acronym of N-Dimensional Array. We have covered Flattening the arrays, Concatenation and Broadcasting etc in detail in the post below.
Find the Numpy post : Link
6. Data Pre-processing
Data preprocessing , one of the first and crucial step — the process in which we prepare the raw data and make it suitable for a ML model to increase its accuracy and efficiency. We have covered Data Cleaning, Data Augmentation, Transformation, Channel Shift etc in detail.
Find the Data Pre-processing post : Link
7. Statistics and Maths with Cheatsheets
There are numerous questions which statistics can help you answer, like ( and the list doesn’t ends here) —
Ads Targeting and optimization — Which ad is more effective in getting people to purchase a product?
Optimization — How can you optimize occupancy in a hotel based on the previous occupancy history data?
Patterns — What the most fitting size of t-shirts based on the what 95% of the population is wearing?
We have covered Linear Algebra, Calculus, Matrix and Vectors, Bayes Theorem and Cheatsheets etc are covered in detail in the post below.
Find the Statistics post : Link
Find the Maths post : Link
8. Neural Networks
This will be starting point of out next post where I’ll build a Neural Network from scratch and cover below points —
Different types of neural networks
Linear Classifiers
Optimization
Hyper Parameter Tuning
That’s it for today. Let me know if you have any questions in the comments section below. Subscribe/ Follow, Like/Clap and Stay Tuned!!
Day 4 coming soon!
Complete System Design Series
6. Networking, How Browsers work, Content Network Delivery ( CDN)
Github —
System Design Case Studies — In Depth
Design Instagram
Design Messenger App
Design Twitter
Some of the other best Series —
30 days of Data Structures and Algorithms and System Design Simplified
Data Science and Machine Learning Research ( papers) Simplified **
100 days : Your Data Science and Machine Learning Degree Series with projects
Complete Data Visualization and Pre-processing Series with projects
Exceptional Github Repos — Part 1
Exceptional Github Repos — Part 2
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For Python Projects —
For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML
Follow for more updates. Stay tuned and keep coding!
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Clustering Geolocation Data in Python using DBSCAN and K-Means
Facial Expression Recognition using Keras
Hyperparameter Tuning with Keras Tuner
Custom Layers in Keras
