TL/DR: Teaching myself about investing and Tensorflow. My model predicts Coca Cola will close $51.35 on 3/19/2021. I have no idea what I'm doing. Do not use this to make financial decisions.

So last week I predicted Coca Cola would close at $42.56. It really closed at $50.36. I would have been better off using the lazy predictor and just assume last week's close of $50.79 would not have moved. See my previous post:

https://www.reddit.com/r/tensorflow/comments/lzv277/silly_prediction_coca_cola_stock_closing_price/

So the first thing I did was scale my numbers. Having price points in the tens of dollars, and volume in the millions blew up my model pretty badly. My previous unscaled version had errors relative to my training data in the 10's of dollars per week.

I scaled volume to shares outstanding, and share price to % change, and it looks like this model is performing better. The scaled model has errors much tighter. I've attached a histogram of my model's error using the last 100 week's of data. Next Friday will be the first real test though.

So the bad news. I define success as my model performing better than a "Lazy" predictor, which assumes that next week's closing price will equal this week's closing price. My model's error histogram pretty much sits exactly on top of the Lazy predictor histogram.

Comparing root mean square error, my LSTM predictor is actually slightly worse...

There are a few known issues that I need to work out. I haven't included dividend data, nor do I have a method to handle splits. I'll need to be able to address splits if I want to include other companies.

Data is starting to get a bit messy to handle. I'm currently stuck doing some manual manipulation and manual data entry, as Alphavantage is missing historical shares outstanding. Ideally I would include float as well.

Looks like I might need to setup a SQL server locally to host all this data.

I'm starting to understand why many of the tutorials spend 80% of their time on data manipulation and only 20% on tensorflow.

Could you just spare me two minutes 🥺 👉👈

I had already installed CUDA v11.8 and it didn't detect my GPU. So today I tried installing CUDA v12.8 and CuDNN v8.9.7.

Specs: GPU --> RTX 3050 Laptop GPU Python --> 3.10 Tensorflow --> 2.18 Visual Studio 2022 installed

Have set up environmental variables. But still my GPU is not getting detected. Tried all the possible ways, asked ChatGPT and deepseek still not got a proper solution. Could anyone in this group help me with this installation process please. Thanks in advance😀

I'm kinda new to Neural Networks and just started to learn coding them by trying some examples. Two weeks ago I was searching for an interesting challenge and I found one. But I'm about to give up because it seems to be too hard for me... But I was curious to know if anyone of you is able to solve this?

https://stackoverflow.com/questions/54381940/tensorflow-convert-html-table-into-json-data

Hi, I'm a new student trying to learn Tensorflow. I have already found some really good books and tutorials which helps me learn this fast. But when I started trying out the examples given I soon realized that all the good tutorials that I have are discussing about Tensorflow 1.15 and amazingly these codes will not work with tensorflow 2.0+.

I really find this cool and amazing behavior from the devolpers who have zero concern about reverse-compatibility. I can go to Google and fix the old code line by line, replacing each with the Tensorflow2 equivalent.

But since I'm a beginner, this is a nightmare for me. Can anyone explain to me in simple terms why these douchebags does not maintain reverse compatibility when these airheads update these libraries?

I really want to find the developers who did this and dip their face in boiling oil.

P.s: when is Tensorflow3 coming out? I'm now trying to learn Tensorflow2, i assume that Tensorflow3 will be completely different from Tensorflow2 and we would have to re-learn everything from scratch in that too.

Thanks for the response guys: I posted this yesterday night when I was super frustrated and angry now I have slept over the rage and I have cooled down. Can someone please suggest me a good TF2 tutorial? I don't like video lectures, because I will sleep if I start. I prefer books or text descriptions followed by small examples something like this, https://www.tutorialspoint.com/tensorflow/tensorflow_tutorial.pdf

This is a really good TF1 tutorial. Can anyone suggest something like that for TF2?

Hey Everyone,

We've been working away on a framework to eliminate or decrease racism in machine learning. Today we have a demo setup and first version release of the library. Would love if anyone has any vision problems classifying people to check out our library to balance out the dataset and keep it private. Any feedback on ways to improve are greatly appreciated.

Demo Site: https://privyfilter.herokuapp.com/

Repo: https://github.com/Deamoner/privyfilter

Architecture and Methodology Article: https://medium.com/@mdavis_71283/hard-problems-racial-bias-in-machine-learning-bf631c9d680c

Things to learn in this project:

- Creating a pipeline for new model creation - first create pipeline to generate dataset and then create model.

Current Features:

- Face Detection

- Demographic Information Extraction

- Synthetic Face Generation

- Face Swapping

Future Features:

- Skin Detection

- Skin Hue Manipulation

- Full Pipeline Process for the entire directory

- Multi-person photo support

- Remove Pipeline and Train pix2pix model

This is an open source initiative, and open to any constructive feedback or even better actually getting your hands dirty by helping code. Looking for any feedback on ways we can improve it for any of your specific use cases.

https://ericmjl.github.io/blog/2019/5/29/reasoning-about-shapes-and-probability-distributions/ 

I am excited. That is all.

We should call the result of a cumulative sum, the jizz.

I finished 12 grade and want to study ML and found that I can get free course of tensorflow and tensorflow certification. I have a good standing in python and I dont want to do bachelor (money problem and the course here is outdated and not taught properly). So is there a remote job in ML, I can get with tensorflow certification only? I am willing to get other certification and learn other courses as long as they are online and free(or cost very less)

Hi folks!

We're a small team in the San Francisco Bay Area looking to better understand the bottlenecks Data Scientists face in their day to day lives while building ML models.

We only need 30 minutes of your time, and in exchange we are willing to offer you a *$15 Gift card for Starbucks or Amazon! (your choice)*

Please fill out this super-short form to help coordinate!

https://forms.gle/L96iEorkr8ttT6bg7

Thank you very much!

How much would it take to train a model which consists of about 2000 pictures on my laptop (I am a beginner but I need to train it for a project) Specs: Ryzen 5 3500u 10gb of ram vega 8 gpu

I was going to do it on the cpu, because I think its more powerful (correct me if I am wrong)

I was doing an exercise by google dev's ml tensorflow course. Im getting this error:

File "c:\Users\shiva\Documents\AI_ML_Tensorflow\TensorflowEx2MNISTComputerVision.py", line 24, in <module>

model.fit(x_train, y_train, epochs=5)

TypeError: Expected uint8, but got 1e-07 of type 'float'.

---------------------------------------------------------------------------------------------------------------------------------------------

Here is the code:

# YOUR CODE SHOULD START HERE
# YOUR CODE SHOULD END HERE
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
# YOUR CODE SHOULD START HERE
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),

tf.keras.layers.Dense(10, activation=tf.nn.softmax)])
# YOUR CODE SHOULD END HERE
model = tf.keras.models.Sequential([
# YOUR CODE SHOULD START HERE

# YOUR CODE SHOULD END HERE
])
model.compile(optimizer = tf.keras.optimizers.Adam(),
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
# YOUR CODE SHOULD START HERE
# YOUR CODE SHOULD END HERE

---------------------------------------------------------------------------------------------------------------------------------------------

I dunno what shud I do? I checked my code but can't find anything that might cause the error. I asked the same question on the r/learnmachinelearning but got no response, Pls help!