Welcome to part 8 of the Deep Learning with Python, Keras, and Tensorflow series. In this tutorial, we’re going to work on using a recurrent neural network to …

36 Comments

What is the ratio this case? What are the two numbers being compared? Why is there another variable `RATIO_TO_PREDICT`?

You are a major positive influence in a world of fake gurus.

Python was the first language I had properly learnt (and still learning). Also, since data science was what sucked me into python, Pandas was one of the first few packages I had started using.

Hence everytime you use 'list(map(fn…' on a series, I wonder, 'why not just .apply' ?

Can anyone comment if this will work for multiple coulmms? Let's say for sports, you have multiple columns for each sport category in basket ball (points, rebounds, assists..) – will the RNN perform succesfully?

One of the very few videos of a guy who knows what he is doing. There are so many videos out there of machine learning noobs, making a trillion mistakes in each and every of their strategies. You got the basics of machine learning right, which was a pleasure to see.

I am trying to implement RNN for batch control. I have 1 input and 4 outputs. All of them are in 50 batches each of length 600 each. So input A (temperature) has data of 50 batches with 600 values for each of the 50 batches. The outputs B,C,D also have the same dimension.

Could you tell me how do I go about preparing appropriate shape/structure of this dataset to implement RNN?

hi when i run the code instead of numbers in the close and future columns i just get NaN. Could you please help me out? LTC-USD_close future time 5777.000000 NaN NaN 5777.770020 NaN NaN 5778.009766 NaN NaN 5778.220215 NaN NaN 5790.009766 NaN NaN 5792.930176 NaN NaN 5795.669922 NaN NaN 5800.000000 NaN NaN 5807.000000 NaN NaN 5809.399902 NaN NaN

Hey, sir. I followed all the codes you have written. But in the end, I got the accuracy of 0.8777 at the first epoch. But yours is 0.5136. This is so wired. I cannot figure out the reason myself.

What is the ratio this case? What are the two numbers being compared? Why is there another variable `RATIO_TO_PREDICT`?

20:15 that's what i want to do

Can anyone tell how to find out the optimal value of the sequence length, instead of giving 60? Thanks

new the new boston bucky:)

where can I find the data used in the model?

You are a major positive influence in a world of fake gurus.

Python was the first language I had properly learnt (and still learning).

Also, since data science was what sucked me into python, Pandas was one of the first few packages I had started using.

Hence everytime you use 'list(map(fn…' on a series, I wonder, 'why not just .apply' ?

Love the videos by the way.

Very nicely explained, thanks.

New sub, ty mind meld complete.

How did you create the data file?

https://pythonprogramming.net/static/downloads/machine-learning-data/crypto_data.zip

To all those getting this error: ValueError: columns overlap but no suffix specified: Index(['BTC-USD_close', 'BTC-USD_volume'], dtype='object')

USE : main_df = main_df.merge(df,left_index=True,right_index=True)

INSTEAD OF : main_df = main_df.join(df)

i think that you have a lots of different cups :))

Can anyone comment if this will work for multiple coulmms? Let's say for sports, you have multiple columns for each sport category in basket ball (points, rebounds, assists..) – will the RNN perform succesfully?

Thank you @Sentex 🙂

One of the very few videos of a guy who knows what he is doing. There are so many videos out there of machine learning noobs, making a trillion mistakes in each and every of their strategies. You got the basics of machine learning right, which was a pleasure to see.

pd.set_option('display.max_rows', 500)

pd.set_option('display.max_columns', 500)

pd.set_option('display.width', 1000)

Amazing stuff bruv

Also Sentdex, what company do you work for?

Sorry if this is a newbie question, but how do you do the F?

was this inspired by https://github.com/wzchen/stock_market_prediction by any chance?

is there an updated data set I can download anywhere? Most of the ones im finding stopped at 2018

Where do you buy the cups haha? Great videos !!!!

df = pd.read_csv(dataset, names=['time','low','hight','open',f'{ratio}_closed',f'{ratio}_volume'],index_col=0,usecols=[0,4,5])

A better way to take the columns.

Any update planned for TensorFlow 2.0?

He called me carl…

How many mugs do you have!😂😂😂

I am trying to implement RNN for batch control. I have 1 input and 4 outputs.

All of them are in 50 batches each of length 600 each.

So input A (temperature) has data of 50 batches with 600 values for each of the 50 batches.

The outputs B,C,D also have the same dimension.

Could you tell me how do I go about preparing appropriate shape/structure of this dataset to implement RNN?

hi when i run the code instead of numbers in the close and future columns i just get NaN. Could you please help me out?

LTC-USD_close future

time

5777.000000 NaN NaN

5777.770020 NaN NaN

5778.009766 NaN NaN

5778.220215 NaN NaN

5790.009766 NaN NaN

5792.930176 NaN NaN

5795.669922 NaN NaN

5800.000000 NaN NaN

5807.000000 NaN NaN

5809.399902 NaN NaN

Hey, sir. I followed all the codes you have written. But in the end, I got the accuracy of 0.8777 at the first epoch. But yours is 0.5136. This is so wired. I cannot figure out the reason myself.

I like how you have a different mug each vid, and it's getting weird but good haha

so where is the rnn？

Hello, would you recommend owncloud or next cloud

your mugs are killing me

Find a Forex or something like that that offers api / build an api over that and let him play with a demo account. If it works try with a true one

probably not better, but it allows you to change the columns easily by dropping them using the list.

names = ["time", "low", "high", "open", "close", "volume"]

ratios = ["BCH-USD", "BTC-USD", "ETH-USD", "LTC-USD"]

x = 0

df_dict = {}

for ratio in ratios:

df = pd.read_csv(f"{ratio}.csv", names = names).drop(columns = names[1:4])

df_dict[f"dataframe{x}"] = df

x += 1

if x > len(ratios) – 1:

pass

else:

df_dict["dataframe0"] = pd.merge(df_dict["dataframe0"], df, on='time', how='left', suffixes = (ratio, ratios[x]))

main_df = df_dict["dataframe0"]

Hey I want to use speech data for recognition using deep learning, please guide me